Genetic risk, adherence to a healthy lifestyle, and type 2 diabetes risk among 550,000 Chinese adults: results from 2 independent Asian cohorts

Evaluation of diabetes mellitus medication-taking behavior among first- and second-generation Australians of Chinese heritage: A nationwide cross-sectional study

Aim

To investigate the association of health literacy, illness perceptions, and beliefs about medications on medication-taking behavior among first- and second-generation Australians of Chinese heritage living with type 2 diabetes mellitus (T2DM).

Method

A nationwide cross-sectional online survey of (N = 455) of whom 196 responded, was conducted among adults (≥18 years) with T2DM of Chinese heritage residing in Australia. Participants were recruited via direct invitation (national registry and specialist clinic). Data collection utilized four validated questionnaires: The Brief Medication Questionnaire, Beliefs about Medicines Questionnaire Specific (BMQ-Specific), Brief Illness Perception 9 Questionnaire (BIPQ), and a 12-item short-form health literacy (HL) questionnaire (HLS-SF12). Bivariate and multivariate analyses were conducted to explore the factors associated with medication-taking.

Results

Overall, 27 % of participants reported missing diabetes medication(s) in the past week, with access barriers most cited (38 %), followed by belief (27 %) and recall (24 %) barriers. Median scores for health literacy, illness perception and beliefs about medications showed problems with health literacy (General Health Literacy Index, median [IQR] =31.94 [26.39ꟷ38.89], a moderate threat to illness perception (BIPQ:= 38.56 ± 10.52) and higher perceived necessity of taking diabetes medications relative to concern (BMQ-Specific Necessity: = 3.80 [3.20-4.20]; BMQ-Specific Concern: = 3.00 [2.50-3.67]). Better medication-taking was seen in people with high necessity beliefs and with low concerns in the use of medications. Health literacy and illness perceptions were not significantly associated with medication-taking behavior.

Conclusion

Medication beliefs play a role in sub-optimal medication-taking behavior among Chinese adults with T2DM. Increased attention needs to be placed on examining and enhancing understanding of diabetes medications while addressing concerns among individuals of Chinese backgrounds to better understand the complexities of medication-taking behavior. Culturally relevant clinical discussion and structured diabetes education may support the development of health promoting medication beliefs potentially supporting optimal medication-taking behavior.

Healthy lifestyle habits, educational attainment, and the risk of 45 age-related health and mortality outcomes in the UK: A prospective cohort study

OBJECTIVES

This study aimed to evaluate to what extent lifestyle habits, contribute to associations between EA and various conditions, and test the variability in risk reduction for specific health conditions linked to a healthy lifestyle across different EA levels.

DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS

Data were analyzed from 341,632 UK Biobank participants without baseline cardiovascular disease or cancer (2006-2010). A healthy lifestyle score (0-5) was created by assigning one point for each of five habits: a healthy diet, sufficient physical activity, non-current smoking, moderate alcohol consumption, and low-risk sleep duration. Baseline data on self-reported and genotype-predicted EA were collected, with 45 health outcomes assessed until January 2021. Logistic regression models were used to assess the relationship between EA and lifestyle habits, and associations between the healthy lifestyle score and health/mortality outcomes were examined using Cox proportional hazards model. Moderation analysis tested whether EA modified the associations between a healthy lifestyle and health outcomes, while mediation analysis estimated the proportion of the association between EA and health outcomes explained by lifestyle habits.

RESULTS

Both self-reported and genotype-predicted EA were associated with a healthy diet, non-current smoking, low-risk sleep duration, and moderate alcohol consumption, but not low-risk physical activity. A healthy lifestyle is inversely linked to risks for 38 of 45 outcomes, including CVD, type 2 diabetes, lung and colon cancer, depression, and chronic kidney disease, as well as overall, CVD, and cancer mortality. Higher EA reduced risk for 25 conditions, such as CVD, certain cancers, chronic liver disease, and fractures; stronger inverse lifestyle-risk associations were observed among less educated individuals. Lifestyle habits explained 47.2% (95% CI: 35.3-59.4%) of the association between genotype-predicted EA and all-cause mortality, mediating a large proportion of associations with CVDs, cancers, dementia, respiratory diseases, and chronic kidney disease.

CONCLUSIONS

Higher EA might encourage the adoption of more healthy lifestyle habits, thus promoting healthy aging. Placing greater emphasis on lifestyle modification is essential for individuals with lower EA to effectively address health inequalities associated with EA.

Genetic predisposition to adiposity, and type 2 diabetes: the role of lifestyle and phenotypic adiposity

AIMS

Genetic predisposition to adiposity is associated with type 2 diabetes (T2D), even in the absence of phenotypic adiposity (obesity and central obesity). We aimed to quantify the overall contribution of obesity and modifiable lifestyle factors to the association between genetic predisposition to adiposity and the development of T2D.

METHODS

This prospective cohort study involved 220 703 White British participants from the UK Biobank. It examined the associations between genetic predisposition to adiposity [body mass index polygenic risk (BMI-PRS) and waist-hip ratio polygenic risk (WHR-PRS)] and incident T2D, as well as interactions and mediation via lifestyle factors (diet quality, physical activity levels, total energy intake, sleep duration, and smoking and alcohol intake) and phenotypic adiposity.

RESULTS

People with high phenotypic adiposity and high adiposity PRS values (>1 SD above the mean) had the highest risk of incident T2D (versus non-obese/central obese and non-high PRS). This was the case for BMI-PRS [hazard ratio (HR) = 3.72] and WHR-PRS (HR = 4.17). Lifestyle factors explained 30.5% of the BMI-PRS/T2D association (2.0% mediation; 28.5% effect modification), and lifestyle and obesity together explained 92.1% (78.8% mediation; 13.3% effect modification). Lifestyle factors explained 20.4% of the WHR-PRS/T2D association (3.4% mediation; 17.0% effect modification), and lifestyle and central obesity together explained 72.8% (41.1% mediation; 31.7% effect modification).

CONCLUSIONS

Whilst phenotypic adiposity explains a large proportion of the association between BMI-PRS/WHR-PRS and T2D, modifiable lifestyle factors also make contributions. Promoting healthy lifestyles among people prone to adiposity is important in reducing the global burden of T2D.

Combined effect of healthy lifestyles and obesity on cardiometabolic risks in Chinese rural adults

BACKGROUND

Combined effect of healthy lifestyles and obesity on cardiometabolic risks were unclear in Chinese rural adults. We aimed to assess the above-mentioned issue.

METHODS

This study included 25,123 adults from baseline survey of Henan rural cohort study. We collected information regarding current not smoking, current not drinking, healthy diet, adequate exercise, and healthy sleep. We calculated the number of healthy lifestyle factors for each participant or used the latent class analysis to identify clustering classes of healthy lifestyle. Body mass index (BMI), waist circumference (WC), blood pressure, blood lipid, and fasting blood glucose were measured. Logistic models were applied to assess the combined associations of healthy lifestyles and obesity with cardiometabolic risks.

RESULTS

3.8%, 45.8%, and 50.4% of all participants had 0-1, 2-3, and 4-5 healthy factors. The prevalence of obesity defined by BMI and WC was 17.1% and 38.1%, respectively. Compared with participants with obesity who met 0-1 healthy factor, those with obesity who met 4-5 healthy factors have a lower risk of hypertension (odds ratio [OR], 0.41; 95% confidence intervals [95%CI], 0.29-0.58) and dyslipidemia (OR, 0.49; 95%CI, 0.35-0.68) except hyperglycemia (OR, 0.87; 95%CI, 0.53-1.43). Irrespective of the healthy lifestyle scores, compared with participants with normal weight, those with obesity were at higher risk of hypertension, dyslipidemia and hyperglycemia. We obtained similar results when using the latent class analysis or WC to define obesity.

CONCLUSION

Our findings indicated that healthy lifestyle did not entirely offset the obesity-related cardiometabolic risks although it brought some benefits.

Associations of Combined Genetic and Lifestyle Risks with Incident Type 2 Diabetes in the UK Biobank

Background

Type 2 diabetes (T2D) results from a complex interplay between genetic predisposition and lifestyle factors. Both genetic susceptibility and unhealthy lifestyle are known to be associated with elevated T2D risk. However, their combined effects on T2D risk are not well studied. We aimed to determine whether unhealthy modifiable health behaviors were associated with similar increases in the risk of incident T2D among individuals with different levels of genetic risk.

Methods

We performed a genetic risk score (GRS) by lifestyle interaction analysis within 332,251 non-diabetic individuals at baseline from the UK Biobank. Multi-ancestry GRS were calculated by summing the effects of 783 T2D-associated variants and ranked into tertiles. We used baseline self-reported data on smoking, BMI, physical activity level, and diet quality to categorize participants as having a healthy, intermediate, or unhealthy lifestyle. Cox proportional hazards regression models were used to generate adjusted hazards ratios (HR) of T2D risk and associated 95% confidence intervals (CI).

Results

During follow-up (median 13.6 years), 13,128 (4.0%) participants developed T2D. GRS (P < 0.001) and lifestyle classification (P < 0.001) were independently associated with increased risk for T2D. Compared with healthy lifestyle, unhealthy lifestyle was associated with increased T2D risk in all genetic risk strata, with adjusted HR ranging from 7.11 (low genetic risk) to 16.33 (high genetic risk).

Conclusions

High genetic risk and unhealthy lifestyle were the most significant contributors to the development of T2D. Individuals at all levels of genetic risk can greatly mitigate their risk for T2D through lifestyle modifications.

Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population

AIM

This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes.

METHODS

This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC).

RESULT

Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732]).

CONCLUSION

Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.

Diabetes in China part 2: prevention, challenges, and progress

During the past 40 years, the prevalence of diabetes in China has increased from less than 1·0% in 1980 to 12·4% in 2018, an increase in line with the rapid growth of the nation's economy. To address such a burden, the Healthy China 2030 initiative and subsequent Action Plan, including a diabetes prevention and control campaign, were launched. A shift from a disease-centred approach to a health-centred approach and from treatment to prevention is the core of the Action Plan and diabetes management in China. In this Review, we discuss the challenges of diabetes prevention in China, including unhealthy lifestyle, increasing young-onset type 2 diabetes, and substantial diabetes care disparities. To address such challenges, countermeasures across different stages of diabetes prevention and targeted at different populations, are needed. Such countermeasures include primordial prevention of risk factors in the general population, primary prevention of diabetes onset in high-risk populations, and secondary prevention of cardiovascular complications for individuals with diabetes. We reflect on China's current progress, strategies, and achievements.

Higher Genetic Risk for Type 2 Diabetes Is Associated With a Faster Decline of β-Cell Function in an East Asian Population

OBJECTIVE

While most genetic variants of type 2 diabetes (T2D) are suggested to be associated with β-cell dysfunction cross sectionally, their association with the longitudinal change of β-cell function remains largely unknown.

RESEARCH DESIGN AND METHODS

We analyzed data from 6,311 participants without T2D at baseline (mean [SD] age 51.6 [8.7] years) from a community-based prospective cohort in Korea. Participants underwent biennial 2-h 75-g oral glucose tolerance tests (OGTTs) during 14 years of follow-up, and the OGTT-derived disposition index (DI) was used as a marker for β-cell function. Genetic risk was quantified using the genome-wide polygenic risk score (PRS) and was stratified into low (1st quintile), intermediate (2nd-4th quintiles), and high (5th quintile) genetic risk. Lifestyle was assessed according to Life's Essential 8.

RESULTS

During a mean follow-up of 10.9 years, 374 (29.6%), 851 (22.5%), and 188 (14.9%) participants developed T2D in the high, intermediate, and low genetic risk groups, respectively. Compared with the low genetic risk group, participants in the high genetic risk group had a 25% lower DI at baseline. Furthermore, in longitudinal analysis, we observed a 1.83-fold faster decline in log2-transformed DI per year (-0.034 vs. -0.019, P = 2.1 × 10-3; per 1-SD increase in T2D PRS, P = 1.2 × 10-4). Healthy lifestyle attenuated the rate of decline in DI across all genetic risk groups.

CONCLUSIONS

Individuals with a higher genetic risk for T2D exhibited not only a lower OGTT-derived β-cell function at baseline but also a notably more rapid decline during follow-up. This information could be used to enable a focused precision prevention with lifestyle intervention.

Galectin-3 impairs calcium transients and β-cell function

In diabetes, macrophages and inflammation are increased in the islets, along with β-cell dysfunction. Here, we demonstrate that galectin-3 (Gal3), mainly produced and secreted by macrophages, is elevated in islets from both high-fat diet (HFD)-fed and diabetic db/db mice. Gal3 acutely reduces glucose-stimulated insulin secretion (GSIS) in β-cell lines and primary islets in mice and humans. Importantly, Gal3 binds to calcium voltage-gated channel auxiliary subunit gamma 1 (CACNG1) and inhibits calcium influx via the cytomembrane and subsequent GSIS. β-Cell CACNG1 deficiency phenocopies Gal3 treatment. Inhibition of Gal3 through either genetic or pharmacologic loss of function improves GSIS and glucose homeostasis in both HFD-fed and db/db mice. All animal findings are applicable to male mice. Here we show a role of Gal3 in pancreatic β-cell dysfunction, and Gal3 could be a therapeutic target for the treatment of type 2 diabetes.

Genetic risk of type 2 diabetes modifies the association between lifestyle and glycemic health at 5 years postpartum among high-risk women

INTRODUCTION

Lifestyle interventions are effective in preventing type 2 diabetes, but genetic background may influence the individual response. In the Finnish gestational diabetes prevention study, RADIEL, lifestyle intervention during pregnancy and first postpartum year was effective in preventing gestational diabetes (GDM) and postpartum glycemic abnormalities only among women at highest genetic risk of type 2 diabetes. This study aimed to assess whether still 5 years postpartum the genetic risk modifies the association between lifestyle and glycemic health.

RESEARCH DESIGN AND METHODS

The RADIEL study (randomized controlled trial) aimed to prevent GDM with a lifestyle intervention among high-risk women (body mass index ≥30 kg/m2 and/or prior GDM). The follow-up study 5 years postpartum included anthropometric measurements, laboratory assessments, device-measured physical activity (PA), and questionnaires. A Healthy Lifestyle Score (HLS) indicated adherence to lifestyle goals (PA, diet, smoking) and a polygenic risk score (PRS) based on 50 type 2 diabetes risk alleles depicted the genetic risk.

RESULTS

Altogether 314 women provided genetic and glycemic data 5 years postpartum. The PRS for type 2 diabetes was not associated with glycemic abnormalities, nor was HLS in the total study sample. There was, however, an interaction between HLS and type 2 diabetes PRS on glycemic abnormalities (p=0.03). When assessing the association between HLS and glycemic abnormalities in PRS tertiles, HLS was associated with reduced risk of glycemic abnormalities only among women at the highest genetic risk (p=0.008).

CONCLUSIONS

These results extend our previous findings from pregnancy and first postpartum year demonstrating that still at 5 years postpartum, healthy lifestyle is associated with a lower risk of prediabetes/diabetes only among women at the highest genetic risk of type 2 diabetes.

Genetic predisposition, lifestyle inflammation score, food-based dietary inflammatory index, and the risk for incident diabetes: Findings from the KoGES data

BACKGROUND AND AIMS

We investigated whether genetic predisposition, the Lifestyle Inflammation Score (LIS), or the Food-based Dietary Inflammatory Index (FDII) were associated with diabetes incidence and whether these factors interact.

METHODS AND RESULTS

The study was conducted using population-based cohort data derived from the Korean Genome and Epidemiology Study, which included 6568 people aged 40-69 years. Based on 25 genetic variants related to diabetes, genetic risk scores (GRSs) were determined and LISs and FDIIs were calculated and stratified into quartiles. We investigated the effects of gene-lifestyle interactions on the incident diabetes. The multivariate Cox proportional hazard model was used to generate hazard ratios with 95 % CIs. During the 16-year follow-up period, diabetes incidence was 13.6 per 1000 person-years. A dose-response association with diabetes was observed for both GRS and LIS quartiles but not for FDII quartiles. The GRS and LIS were also independently associated with diabetes incidence in a multivariate model. Compared to the bottom quartile, the top LIS quartile and the top GRS quartile had a 2.4-fold (95 % CI, 2.0-2.8) and a 1.4-fold (95 % CI, 1.2-1.7) higher diabetes risk, respectively. However, the FDII exhibited null association. When each genetic variant was evaluated, the top versus bottom LIS quartiles exhibited heterogeneous diabetes risks for rs560887 within G6PC2, rs7072268 within HK1, and rs837763 within CDT1; however, these differences were not statistically significant in multiple comparison.

CONCLUSION

Both GRS and LIS factors independently affect the incident diabetes, but their interaction effect showed insignificant association. Therefore, regardless of genetic susceptibility, more effort is needed to lower the risk for diabetes by improving lifestyle behaviors.

Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study

AIM

The influence of family history of diabetes, probably reflecting genetic and lifestyle factors, on the association of combined genetic and lifestyle risks with diabetes is unknown. We examined these associations.

METHODS

This cross-sectional study included 9,681 participants in the Tohoku Medical Megabank Community-based Cohort Study. A lifestyle score, which was categorized into ideal, intermediate, and poor lifestyles, was given. Family history was obtained through a self-reported questionnaire. A polygenic risk score (PRS) was constructed in the target data (n=1,936) using publicly available genome-wide association study summary statistics from BioBank Japan. For test data (n=7,745), we evaluated PRS performance and examined the associations of combined family history and genetic and lifestyle risks with diabetes. Diabetes was defined as non-fasting blood glucose ≥ 200 mmHg, HbA1c ≥ 6.5%, and/or self-reported diabetes treatment.

RESULTS

In test data, 467 (6.0%) participants had diabetes. Compared with a low genetic risk and an ideal lifestyle without a family history, the odds ratio (OR) was 3.73 (95% confidence interval [CI], 1.92-7.00) for a lower genetic risk and a poor lifestyle without a family history. Family history was significantly associated with diabetes (OR, 3.58 [95% CI, 1.73-6.98]), even in those with a low genetic risk and an ideal lifestyle. Even among participants who had an ideal lifestyle without a family history, a high genetic risk was associated with diabetes (OR, 2.49 [95% CI, 1.65-3.85]). Adding PRS to family history and conventional lifestyle risk factors improved the prediction ability for diabetes.

CONCLUSIONS

Our findings support the notion that a healthy lifestyle is important to prevent diabetes regardless of genetic risk.

Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study

BACKGROUND

Lifestyle and longevity genes have different and important roles in the human lifespan; however, the association between a healthy lifestyle in late-life and life expectancy mediated by genetic risk is yet to be elucidated. We aimed to investigate the associations of healthy lifestyle in late-life and genetic risk with life expectancy among older adults.

METHODS

A weighted healthy lifestyle score was constructed from the following variables: current non-smoking, non-harmful alcohol consumption, regular physical activity, and a healthy diet. Participants were recruited from the Chinese Longitudinal Healthy Longevity Survey, a prospective community-based cohort study that took place between 1998 and 2018. Eligible participants were aged 65 years and older with available information on lifestyle factors at baseline, and then were categorised into unhealthy (bottom tertile of the weighted healthy lifestyle score), intermediate (middle tertile), and healthy (top tertile) lifestyle groups. A genetic risk score was constructed based on 11 lifespan loci among 9633 participants, divided by the median and classified into low and high genetic risk groups. Stratified Cox proportional hazard regression was used to estimate the interaction between genetic and lifestyle factors on all-cause mortality risk.

FINDINGS

Between Jan 13, 1998, and Dec 31, 2018, 36 164 adults aged 65 years and older were recruited, among whom a total of 27 462 deaths were documented during a median follow-up of 3·12 years (IQR 1·62-5·94) and included in the lifestyle association analysis. Compared with the unhealthy lifestyle category, participants in the healthy lifestyle group had a lower all-cause mortality risk (hazard ratio [HR] 0·56 [95% CI 0·54-0·57]; p<0·0001). The highest mortality risk was observed in individuals in the high genetic risk and unhealthy lifestyle group (HR 1·80 [95% CI 1·63-1·98]; p<0·0001). The absolute risk reduction was greater for participants in the high genetic risk group. A healthy lifestyle was associated with a gain of 3·84 years (95% CI 3·05-4·64) at the age of 65 years in the low genetic risk group, and 4·35 years (3·70-5·06) in the high genetic risk group.

INTERPRETATION

A healthy lifestyle, even in late-life, was associated with lower mortality risk and longer life expectancy among Chinese older adults, highlighting the importance of a healthy lifestyle in extending the lifespan, especially for individuals with high genetic risk.

FUNDING

National Natural Science Foundation of China.

TRANSLATION

For the Mandarin translation of the abstract see Supplementary Materials section.

Healthy Lifestyle Score and Glycemic Control in Type 2 Diabetes Mellitus Patients: A City-Wide Survey in China

BACKGROUND

Few studies have investigated the combined impact of healthy lifestyle factors on glycemic control. Our study aimed to examine the associations of a healthy lifestyle score (HLS) with glycemic control and to explore the interactive effects of lifestyle factors among patients with type 2 diabetes mellitus (T2DM) in China.

METHODS

This cross-sectional study was conducted among T2DM patients based on the health management of residents from Guangzhou, China. Good glycemic control was defined as fasting plasma glucose < 7.0 mmol/L. HbA1c < 7.0% was also defined as good glycemic control in sensitivity analysis. The HLS was defined as including physical activity, waist circumference, body mass index, dietary habit, smoking, and alcohol consumption. Logistic regression models were used to examine the associations and interactions between the lifestyle factors and glycemic control.

RESULTS

Compared with participants with an HLS ≤ 2, the odds ratios (95% confidence intervals) for an HLS of 3, 4, 5, and 6 were 0.82 (0.77-0.87), 0.74 (0.70-0.79), 0.61 (0.57-0.65), and 0.56 (0.53-0.60), respectively. Significant interactions of healthy lifestyle factors in relation to glycemic control were shown (P_interaction < 0.05).

CONCLUSIONS

A healthier lifestyle was significantly associated with good glycemic control in patients with T2DM, and combined healthy lifestyle factors had a better effect than considering them individually.

Gene-Diet Interactions on Metabolic Disease-Related Outcomes in Southeast Asian Populations: A Systematic Review

Diabetes and obesity are chronic diseases that are a burden to low- and middle-income countries. We conducted this systematic review to understand gene-diet interactions affecting the Southeast Asian population's risk of obesity and diabetes. The literature search was performed on Google Scholar and MEDLINE (PubMed) search engines independently by four reviewers who evaluated the eligibility of articles based on inclusion criteria. Out of 19,031 articles, 20 articles examining gene-diet interactions on obesity and/or diabetes-related traits met the inclusion criteria. Three (Malaysia, Indonesia, and Singapore) out of eleven Association of Southeast Asian Nations (ASEAN) countries have conducted studies on gene-diet interactions on obesity and diabetes. From the 20 selected articles, the most common interactions were observed between macronutrients and genetic risk score (GRS) on metabolic disease-related traits in the Malay, Chinese, and Indian ethnicities. Overall, we identified 29 significant gene-diet interactions in the Southeast Asian population. The results of this systematic review demonstrate ethnic-specific gene-nutrient interactions on metabolic-disease-related traits in the Southeast Asian population. This is the first systematic review to explore gene-diet interactions on obesity and diabetes in the Southeast Asian population and further research using larger sample sizes is required for better understanding and framing nutrigenetic approaches for personalized nutrition.

Adherence to the EAT-Lancet diet, genetic susceptibility, and risk of type 2 diabetes in Swedish adults

BACKGROUND AND AIMS

In 2019, the EAT-Lancet Commission proposed a mainly plant-based diet that nurtures human health and supports environmental sustainability. However, its association with type 2 diabetes (T2D) has not been widely studied, and it remains unclear whether genetic susceptibility for T2D can modify this association. The aim was therefore to investigate the association between the EAT-Lancet diet and risk of T2D and assess whether the association differs by the genetic predisposition to T2D.

METHODS

A total of 24,494 participants from the Malmö Diet and Cancer study were analyzed. Dietary intake was assessed using a modified diet history methodology, and an EAT-Lancet diet index (range from 0 to 42 points) was constructed based on the EAT-Lancet reference diet. National and local registers were used to identify T2D cases during follow-up. Cox proportional hazards regression model was applied to estimate the association between the EAT-Lancet diet index and risk of T2D. Genetic predisposition to T2D was captured based on 116 single nucleotide polymorphisms.

RESULTS

During a median of 24.3 years of follow-up, 4197 (17.1 %) T2D cases were documented. Compared with those with the lowest adherence to the EAT-Lancet diet (≤13 points), participants who had the highest adherence (≥23 points) showed an 18 % (95 % CI: 4 %-30 %) lower risk of T2D (P for trend <0.01). There was no significant multiplicative interaction between genetic predisposition to T2D and the EAT-Lancet diet index (P = 0.59). Also, no significant additive interaction between the genetic risk and the EAT-Lancet diet was seen (P = 0.44). The highest risk was observed among the 22.9 % of the individuals with high genetic risk and low EAT-Lancet diet score (HR = 1.79; 95 % CI: 1.63, 1.96).

CONCLUSIONS

Our findings indicate that high adherence to the EAT-Lancet diet was associated with decreased risk of incident T2D among people with different genetic risks.

Associations Between Modifiable Risk Factors and Changes in Glycemic Status Among Individuals With Prediabetes

OBJECTIVE

To examine the associations between modifiable risk factors and glycemic status changes in individuals with prediabetes.

RESEARCH DESIGN AND METHODS

A total of 10,358 individuals with prediabetes defined by their fasting blood glucose and HbA1c levels from the Health Examinees-Gem study were included in the present study. Modifiable factors, including BMI, abdominal obesity, smoking status, physical activity, alcohol consumption, diet quality, hypertension, and dyslipidemia, were examined to determine their associations with changes in glycemic status during follow-up. In addition, modifiable-factor scores were calculated, and their association with changes in glycemic status was also analyzed.

RESULTS

The median follow-up time for this study was 4 years (range, 1-7 years). BMI ≥25 kg/m2 (adjusted odds ratio [OR] 0.71 [95% CI 0.63-0.79]), abdominal obesity (OR 0.76 [95% CI 0.68-0.86]), heavy drinking (OR 0.74 [95% CI 0.60-0.91]), hypertension (OR 0.71 [95% CI 0.64-0.79]), and dyslipidemia (OR 0.78 [95% CI 0.70-0.85]) were associated with a lower possibility of normoglycemia reversion. BMI ≥25 kg/m2 (OR 1.58 [95% CI 1.29-1.94]), abdominal obesity (OR 1.31 [95% CI 1.11-1.55]), current smoking (OR 1.43 [95% CI 1.07-1.91]), and hypertension (OR 1.26 [95% CI 1.07-1.49]) were associated with a higher probability of type 2 diabetes progression. Having more favorable modifiable factors was also associated with normoglycemia reversion (OR 1.46 [95% CI 1.30-1.64]) and type 2 diabetes progression (OR 0.62 [95% CI 0.49-0.77]).

CONCLUSIONS

More favorable modifiable factors were related to a higher probability of returning to normoglycemia and a lower probability of progression to type 2 diabetes.

Genetic Risk, Adherence to a Healthy Lifestyle, and Hyperuricemia: The TCLSIH Cohort Study

BACKGROUND

Genetic factors have been associated with hyperuricemia in large studies, but the extent to which this can be offset by a healthy lifestyle is unknown. This study aimed to examine whether healthy lifestyle could reduce hyperuricemia risk among individuals with different genetic profiles.

METHODS

We defined a lifestyle score using body mass index, smoking, alcohol consumption, physical activities, and diets in 2796 unrelated individuals from the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort study. Polygenic risk scores (PRS) were constructed based on uric acid loci. Associations of combined lifestyle factors and genetic risk and incident hyperuricemia were estimated using Cox proportional hazard regression.

RESULTS

Of 2796 individuals, 747 participants (26.7%) developed hyperuricemia. Genetic risk and lifestyle were predictors of incident events, and they showed an interaction for the outcome. Compared with high PRS, low PRS reduced risk of incident hyperuricemia by 40%, and compared with unhealthy lifestyle, healthy lifestyle reduced risk of incident hyperuricemia by 41%. Compared with unhealthy lifestyle and high genetic risk, adherence to healthy lifestyle was associated with a 68% (95% confidence interval, 44%-81%) lower risk of hyperuricemia among those at a low genetic risk.

CONCLUSIONS

In this prospective cohort study, we observed an interaction between genetics and lifestyle and the risk of hyperuricemia. The public health implication is that a healthy lifestyle is important for hyperuricemia prevention, especially for individuals with high genetic risk scores.

Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type

The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999-2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients.

Associations of genetic susceptibility and healthy lifestyle with incidence of coronary heart disease and stroke in individuals with hypertension

AIMS

This study explored the associations of genetic susceptibility and adherence to a healthy lifestyle with incident coronary heart disease (CHD) and stroke in individuals with hypertension.

METHODS AND RESULTS

This study included 258 531 European descendants with hypertension at baseline from UK Biobank. Genetic risk of CHD and stroke was estimated using polygenic risk scores derived from 300 and 87 single-nucleotide polymorphisms, respectively. Lifestyle scores were calculated based on 4 lifestyle components (no obesity, no current smoking, regular physical activity and healthy diet). Cox regression with age as the underlying timescale was fit for incident CHD (n = 7470) and stroke (n = 5015), separately. A favourable lifestyle (3-4 lifestyle components) was associated with 37% and 30% lower hazards of CHD (95% confidence intervals, 32-42%) and stroke (23-37%), compared with an unfavourable lifestyle (0-1 lifestyle component), at all levels of genetic risk. Evidence of interaction between genetic susceptibility and lifestyle adherence was found for stroke (P = 0.036): no evidence of interaction for CHD (P = 0.524). A favourable lifestyle at high genetic risk had lower 12-year absolute risk of CHD and stroke, compared with an unfavourable lifestyle at low-to-intermediate genetic (exception: an unfavourable lifestyle at low CHD genetic risk).

CONCLUSION

Adhering to a healthy lifestyle is associated with lower CHD and stroke risk regardless of genetic risk among individuals with hypertension. Risk of CHD and stroke for those at high genetic risk but adhering to a healthy lifestyle was generally lower than for those at low-to-intermediate genetic risk but adhering to an unhealthy lifestyle.

Modifiable risk factors and long term risk of type 2 diabetes among individuals with a history of gestational diabetes mellitus: prospective cohort study

OBJECTIVES

To evaluate the individual and combined associations of five modifiable risk factors with risk of type 2 diabetes among women with a history of gestational diabetes mellitus and examine whether these associations differ by obesity and genetic predisposition to type 2 diabetes.

DESIGN

Prospective cohort study.

SETTING

Nurses' Health Study II, US.

PARTICIPANTS

4275 women with a history of gestational diabetes mellitus, with repeated measurements of weight and lifestyle factors and followed up between 1991 and 2009.

MAIN OUTCOME MEASURE

Self-reported, clinically diagnosed type 2 diabetes. Five modifiable risk factors were assessed, including not being overweight or obese (body mass index <25.0), high quality diet (top two fifthsof the modified Alternate Healthy Eating Index), regular exercise (≥150 min/week of moderate intensity or ≥75 min/week of vigorous intensity), moderate alcohol consumption (5.0-14.9 g/day), and no current smoking. Genetic susceptibility for type 2 diabetes was characterised by a genetic risk score based on 59 single nucleotide polymorphisms associated with type 2 diabetes in a subset of participants (n=1372).

RESULTS

Over a median 27.9 years of follow-up, 924 women developed type 2 diabetes. Compared with participants who did not have optimal levels of any of the risk factors for the development of type 2 diabetes, those who had optimal levels of all five factors had >90% lower risk of the disorder. Hazard ratios of type 2 diabetes for those with one, two, three, four, and five optimal levels of modifiable factors compared with none was 0.94 (95% confidence interval 0.59 to 1.49), 0.61 (0.38 to 0.96), 0.32 (0.20 to 0.51), 0.15 (0.09 to 0.26), and 0.08 (0.03 to 0.23), respectively (P_trend<0.001). The inverse association of the number of optimal modifiable factors with risk of type 2 diabetes was seen even in participants who were overweight/obese or with higher genetic susceptibility (P_trend<0.001). Among women with body mass index ≥25 (n=2227), the hazard ratio for achieving optimal levels of all the other four risk factors was 0.40 (95% confidence interval 0.18 to 0.91). Among women with higher genetic susceptibility, the hazard ratio of developing type 2 diabetes for having four optimal factors was 0.11 (0.04 to 0.29); in the group with optimal levels of all five factors, no type 2 diabetes events were observed.

CONCLUSIONS

Among women with a history of gestational diabetes mellitus, each additional optimal modifiable factor was associated with an incrementally lower risk of type 2 diabetes. These associations were seen even among individuals who were overweight/obese or were at greater genetic susceptibility.

Impact of healthy lifestyle on the risk of type 2 diabetes mellitus in southwest China: A prospective cohort study

AIMS

To explore the influence of nine healthy lifestyle factors on the risk of type 2 diabetes mellitus in adults in Guizhou, China.

METHODS

Data were obtained from a large population-based prospective cohort study in Guizhou Province, China. A total of 7,319 participants aged ≥18 years without diabetes at baseline were included in this study and were followed up from 2016 to 2020. A healthy lifestyle score was calculated based on the number of healthy lifestyle factors.

RESULTS

During an average of 7.1 person-years of follow-up, 764 participants were diagnosed with type 2 diabetes mellitus. Compared with those of participants who scored 0-3 for a healthy lifestyle, the hazard ratios (95% confidence intervals) of those who scored 4, 5, 6, and ≥7 were 0.676 (0.523-0.874), 0.599 (0.464-0.773), 0.512 (0.390-0.673), and 0.393 (0.282-0.550), respectively, showing a gradual downward trend (P for trend <0.01). More importantly, they had lower fasting and 2 h post-load plasma glucose levels and fewer changes in plasma glucose levels during follow-up. If ≥7 healthy lifestyle factors were maintained, 33.8% of incident diabetes cases could have been prevented. Never smoking was the strongest protective factor against type 2 diabetes mellitus.

CONCLUSIONS

A healthy lifestyle can effectively decrease plasma glucose levels and reduce the incidence of type 2 diabetes mellitus in adults in Guizhou, China. In addition, not smoking may be an effective way to prevent type 2 diabetes mellitus.

Joint impact of BMI, physical activity and diet on type 2 diabetes: Findings from two population-based cohorts in China

AIMS

Limited epidemiological data on the combined impact of several lifestyle factors on type 2 diabetes (T2D) incidence was reported in Chinese population. This study aimed to examine how combinations of BMI, physical activity and diet relate to T2D incidence and estimate corresponding population attributable risk in the general population.

METHODS

A total of 56,691 male and 70,849 female participants aged 40-74 years old in two population-based cohorts from the Shanghai Men's and Women's Health Studies were used for analysis. The Cox regression model was used to estimate the association between lifestyle factors collected at baseline and T2D incidence. Multivariable-adjusted population attributable risks were calculated for specific combinations of lifestyle factors.

RESULTS

There were 3315 male and 5925 female incident T2D, with corresponding density incidence rates of 6.39 and 6.04 per 1000 person-years. If the healthiest group of healthy lifestyle index (HLI) was used as a reference, the hazard ratios (95% confidence intervals) of T2D increased monotonically in men [2.04 (1.75, 2.38); 2.94 (2.53, 3.42); 4.31 (3.66, 5.07)] and women [1.85 (1.64, 2.08); 2.79 (2.49, 3.13); 4.14 (3.66, 4.67)]. One point increase of HLI was related to 35% and 35% lower risk in men and women. About 52.7% and 58.4% cases in men and women could have been avoided if participants had been adherent to a healthy lifestyle of maintaining healthy body weight, eating a healthy diet and keeping physically active.

CONCLUSIONS

An increased number of healthy lifestyle factors were associated with a decreased risk of T2D in the Chinese population. Future interventions targeted at combined healthy lifestyle factors are needed to reduce the burden of T2D.

Predictors of incident diabetes in two populations: framingham heart study and hispanic community health study / study of latinos

BACKGROUND

Non-genetic factors contribute to differences in diabetes risk across race/ethnic and socioeconomic groups, which raises the question of whether effects of predictors of diabetes are similar across populations. We studied diabetes incidence in the primarily non-Hispanic White Framingham Heart Study (FHS, N = 4066) and the urban, largely immigrant Hispanic Community Health Study/Study of Latinos (HCHS/SOL, N = 6891) Please check if the affiliations are captured and presented correctly.

METHODS

Clinical, behavioral, and socioeconomic characteristics were collected at in-person examinations followed by seven-day accelerometry. Among individuals without diabetes, Cox proportional hazards regression models (both age- and sex-adjusted, and then multivariable-adjusted for all candidate predictors) identified predictors of incident diabetes over a decade of follow-up, defined using clinical history or laboratory assessments.

RESULTS

Four independent predictors were shared between FHS and HCHS/SOL. In each cohort, the multivariable-adjusted hazard of diabetes increased by approximately 50% for every ten-year increment of age and every five-unit increment of body mass index (BMI), and was 50-70% higher among hypertensive than among non-hypertensive individuals (all P < 0.01). Compared with full-time employment status, the multivariable-adjusted hazard ratio (HR) and 95% confidence interval (CI) for part-time employment was 0.61 (0.37,1.00) in FHS and 0.62 (0.41,0.95) in HCHS/SOL. Moderate-to-vigorous physical activity (MVPA) was an additional predictor in common observed in age- and sex-adjusted models, which did not persist after adjustment for other covariates (compared with MVPA ≤ 5 min/day, HR for MVPA level ≥ 30 min/day was 0.48 [0.31,0.74] in FHS and 0.74 [0.56,0.97] in HCHS/SOL). Additional predictors found in sex- and age-adjusted analyses among the FHS participants included male gender and lower education, but these predictors were not found to be independent of others in multivariable adjusted models, nor were they associated with diabetes risk among HCHS/SOL adults.

CONCLUSIONS

The same four independent predictors - age, body mass index, hypertension and employment status - were associated with diabetes risk across two disparate US populations. While the reason for elevated diabetes risk in full-time workers is unclear, the findings suggest that diabetes may be part of the work-related burden of disease. Our findings also support prior evidence that differences by gender and socioeconomic position in diabetes risk are not universally present across populations.

Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

BACKGROUND

Both genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes.

METHODS AND FINDINGS

We analyzed data from 35,759 men and women in the United States participating in the Nurses' Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (Pinteraction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (Pinteraction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data.

CONCLUSIONS

These data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk.

Genistein-Opportunities Related to an Interesting Molecule of Natural Origin

Nowadays, increasingly more attention is being paid to a holistic approach to health, in which diet contributes to disease prevention. There is growing interest in functional food that not only provides basic nutrition but has also been demonstrated to be an opportunity for the prevention of disorders. A promising functional food is soybean, which is the richest source of the isoflavone, genistein. Genistein may be useful in the prevention and treatment of such disorders as psoriasis, cataracts, cystic fibrosis, non-alcoholic fatty liver disease and type 2 diabetes. However, achievable concentrations of genistein in humans are low, and the use of soybean as a functional food is not devoid of concerns, which are related to genistein's potential side effects resulting from its estrogenic and goitrogenic effects.

Association between exposure to earthquake in early life and diabetes mellitus incidence in adulthood with the modification of lifestyles: Results from the Kailuan study

BACKGROUND

Exposure to disasters in early life may induce lifetime health risk, but investigation on earthquake exposure and DM in later life is still limited. The aim of the current study is to evaluate the association between exposure to the Tangshan Earthquake in early life and diabetes mellitus (DM) incidence in adulthood, and explore the modification of lifestyles on DM development.

METHODS

Participants who were free of DM at baseline from the Kailuan Study were included in this study. All participants were divided into fetal-exposed, infant-exposed, early childhood-exposed and nonexposed group. The effect of earthquake exposure on DM and modification of lifestyles were examined by multivariable-adjusted Cox proportional hazard model.

RESULTS

The exposed group had a higher risk of DM than nonexposed group, especially in infant-exposed and early childhood-exposed group, with hazard ratio (HR) of 1.62 [95% confidence intervals (CI), 1.21-2.17] and 1.46 (95% CI, 1.06-1.99), respectively. After stratifying by lifestyles, a significant modification was observed in alcohol consumption.

CONCLUSION

Exposing to earthquake in early life could increase DM incidence in later life, and alcohol consumption might modify the effect of earthquake exposure on DM development. More attention should be paid on the preventions of DM among adults who exposed to earthquake in their early life.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Effect of Diet Quality and Genetic Predisposition on Hemoglobin A1c and Type 2 Diabetes Risk: Gene-Diet Interaction Analysis of 357,419 Individuals

OBJECTIVE

To assess the interactions between diet quality and genetic predisposition to incident type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

Between 2006 and 2010, 357,419 participants with genetic and complete dietary data from the UK Biobank were enrolled and prospectively followed up to 2017. The genetic risk score (GRS) was calculated on the basis of 424 variants associated with T2D risk, and a higher GRS indicates a higher genetic predisposition to T2D. The adherence to a healthy diet was assessed by a diet quality score comprising 10 important dietary components, with a higher score representing a higher overall diet quality.

RESULTS

There were 5,663 incident T2D cases documented during an average of 8.1 years of follow-up. A significant negative interaction was observed between the GRS and the diet quality score. After adjusting for major risk factors, per SD increment in the GRS and the diet quality score was associated with a 54% higher and a 9% lower risk of T2D, respectively. A simultaneous increment of 1 SD in both the diet quality score and GRS was additionally associated with a 3% lower T2D risk due to the antagonistic interaction. In categorical analyses, a sharp reduction of 23% in T2D risk associated with a 1-SD increment in the diet quality score was detected among participants in the extremely high GRS group (GRS >95%). We also observed a strong negative interaction between the GRS and the diet quality score on the blood HbA1c level at baseline (P < 0.001).

CONCLUSIONS

The adherence to a healthy diet was associated with more reductions in blood HbA1c levels and subsequent T2D risk among individuals with a higher genetic risk. Our findings support tailoring dietary recommendations to an individual's genetic makeup for T2D prevention.

Cardiovascular health, genetic predisposition, and lifetime risk of type 2 diabetes

AIMS

Data on the lifetime risk of type 2 diabetes (T2D) incidence across different cardiovascular health (CVH) categories are scarce. Moreover, it remains unclear whether a genetic predisposition modifies this association.

METHODS AND RESULTS

Using data from the prospective population-based Rotterdam Study, a CVH score (body mass index, blood pressure, total cholesterol, smoking status, diet, and physical activity) was calculated and further categorized at baseline. Genetic predisposition to T2D was assessed and divided into tertiles by creating a genetic risk score (GRS). We estimated the lifetime risk for T2D within different CVH and GRS categories. Among 5993 individuals free of T2D at baseline [mean (standard deviation) age, 69.1 (8.5) years; 58% female], 869 individuals developed T2D during follow-up. At age 55 years, the remaining lifetime risk of T2D was 22.6% (95% CI: 19.4-25.8) for ideal, 28.3% (25.8-30.8) for intermediate, and 32.6% (29.0-36.2) for poor CVH. After further stratification by GRS tertiles, the lifetime risk for T2D was still the lowest for ideal CVH in the lowest GRS tertiles [21.5% (13.7-29.3)], in the second GRS tertile [20.8% (15.9-25.8)], and in the highest tertile [23.5% (18.5-28.6)] when compared with poor and intermediate CVH.

CONCLUSION

Our results highlight the importance of favourable CVH in preventing T2D among middle-aged individuals regardless of their genetic predisposition.

Lifestyle Score and Genetic Factors With Hypertension and Blood Pressure Among Adults in Rural China

Background: Although high genetic risk and unhealthful lifestyles are associated with a high risk of hypertension, but the combined relationship between lifestyle score and genetic factors on blood pressure remains limited, especially in resource-constrained areas. Aim: To explore the separate and joint effects between genetic and lifestyle factors on blood pressure and hypertension in rural areas. Methods: In 4,592 adults from rural China with a 3-year of follow-up, a genetic risk score (GRS) was established using 13 single nucleotide polymorphisms (SNPs) and the lifestyle score was calculated including factors diet, body mass index (BMI), smoking status, drinking status, and physical activity. The associations of genetic and lifestyle factors with blood pressure and hypertension were determined with generalized linear and logistic regression models, respectively. Results: The high-risk GRS was found to be associated with evaluated blood pressure and hypertension and the healthful lifestyle with diastolic blood pressure (DBP) level. Individuals with unhealthful lifestyles in the high GRS risk group had an odds ratio (OR) (95% CI) of 1.904 (1.006, 3.603) for hypertension than those with a healthful lifestyle in the low GRS risk group. Besides, the relative risk (RR), attributable risk (AR), and population attributable risk (PAR) for unhealthful lifestyle are 1.39, 5.87, 0.04%, respectively, and the prevented fraction for the population (PFP) for healthful lifestyle is 9.47%. Conclusion: These results propose a joint effect between genetic and lifestyle factors on blood pressure and hypertension. The findings provide support for adherence to a healthful lifestyle in hypertension precision prevention. Clinical Trial Registration: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.

Lower Dietary Intake of Plant Protein Is Associated with Genetic Risk of Diabetes-Related Traits in Urban Asian Indian Adults

The increasing prevalence of type 2 diabetes among South Asians is caused by a complex interplay between environmental and genetic factors. We aimed to examine the impact of dietary and genetic factors on metabolic traits in 1062 Asian Indians. Dietary assessment was performed using a validated semi-quantitative food frequency questionnaire. Seven single nucleotide polymorphisms (SNPs) from the Transcription factor 7-like 2 and fat mass and obesity-associated genes were used to construct two metabolic genetic risk scores (GRS): 7-SNP and 3-SNP GRSs. Both 7-SNP GRS and 3-SNP GRS were associated with a higher risk of T2D (p = 0.0000134 and 0.008, respectively). The 3-SNP GRS was associated with higher waist circumference (p = 0.010), fasting plasma glucose (FPG) (p = 0.002) and glycated haemoglobin (HbA1c) (p = 0.000066). There were significant interactions between 3-SNP GRS and protein intake (% of total energy intake) on FPG (Pinteraction = 0.011) and HbA1c (Pinteraction = 0.007), where among individuals with lower plant protein intake (<39 g/day) and those with >1 risk allele had higher FPG (p = 0.001) and HbA1c (p = 0.00006) than individuals with ≤1 risk allele. Our findings suggest that lower plant protein intake may be a contributor to the increased ethnic susceptibility to diabetes described in Asian Indians. Randomised clinical trials with increased plant protein in the diets of this population are needed to see whether the reduction of diabetes risk occurs in individuals with prediabetes.

Early-life exposure to the Chinese famine, genetic susceptibility and the risk of type 2 diabetes in adulthood

AIMS/HYPOTHESIS

Early famine exposure has been related to the development of type 2 diabetes; however, little is known about whether the genetic background modifies this association. We aimed to investigate the joint effects of famine exposure at different stages of early life and genetic susceptibility on diabetes risk in adulthood.

METHODS

The study included 8350 participants from the Survey on Prevalence in East China for Metabolic Diseases and Risk Factors (SPECT-China) who were born around the time of the Chinese Great Famine. We determined famine exposure subgroups according to the birth year as nonexposed (1963-1974), fetal-exposed (1959-1962), childhood-exposed (1949-1958), and adolescence-exposed (1941-1948). We developed a genetic risk score of 21 variants previously associated with type 2 diabetes in East Asians. Hierarchical logistic models were used to examine the association of famine exposure and genetic risk with diabetes.

RESULTS

The age-standardised prevalence of diabetes in nonexposed, fetal-exposed, childhood-exposed and adolescence-exposed subgroups was 13.0%, 18.2%, 15.1% and 13.2%, respectively. Compared with nonexposed participants, fetal-exposed participants showed an increased risk of diabetes in adulthood (OR 1.47; 95% CI 1.13, 1.93). A higher genetic risk score was associated with an increased risk of diabetes (OR 1.23; 95% CI 1.15, 1.31 per SD increment). The association between famine exposure and diabetes was consistent across genetic risk strata (all p for interaction >0.05). When considered jointly, fetal- or childhood-exposed participants at high genetic risk (highest tertile of genetic risk score) had 2.60-fold (95% CI 1.71, 3.93) and 1.95-fold (95% CI 1.24, 3.05) higher risks of diabetes, respectively, compared with nonexposed participants at low genetic risk (lowest tertile).

CONCLUSIONS/INTERPRETATIONS

Prenatal exposure to famine was associated with an increased risk of type 2 diabetes in Chinese adults independent of genetic risk score using 21 variants common in the East Asian population. Famine exposure and genetic susceptibility may exhibit an additive effect on diabetes development.

Dietary Soy Consumption and Cardiovascular Mortality among Chinese People with Type 2 Diabetes

Randomized controlled trials showed that soy intervention significantly improved blood lipids in people with diabetes. We sought to prospectively examine the association of soy consumption with the risk of cardiovascular death among individuals with diabetes. A total of 26,139 participants with a history of diabetes were selected from the Chinese Kadoorie Biobank study. Soy food consumption was assessed by a food frequency questionnaire. Causes of death were coded by the 10th International Classification of Diseases. The Cox proportional hazard regression was used to compute the hazard ratios. During a median follow-up of 7.8 years, a total of 1626 deaths from cardiovascular disease (CVD) were recorded. Compared with individuals who never consumed soy foods, the multivariable-adjusted risks (95% confidence intervals) of CVD mortality were 0.92 (0.78, 1.09), 0.89 (0.75, 1.05), and 0.77 (0.62, 0.96) for those who consumed soy foods monthly, 1–3 days/week, and ≥4 days/week, respectively. For cause-specific cardiovascular mortality, significant inverse associations were observed for coronary heart disease and acute myocardial infarction. Higher soy food consumption was associated with a lower risk of cardiovascular death, especially death from coronary heart disease and acute myocardial infarction, in Chinese adults with diabetes.

Combined lifestyle factors and the risk of LADA and type 2 diabetes - Results from a Swedish population-based case-control study

AIMS

We investigated the risk of latent autoimmune diabetes in adults (LADA) and type 2 diabetes in relation to a healthy lifestyle, the proportion of patients attributable to an unhealthy lifestyle, and the influence of family history of diabetes (FHD) and genetic susceptibility.

METHODS

The population-based study included incident LADA (n = 571), type 2 diabetes (n = 1962), and matched controls (n = 2217). A healthy lifestyle was defined by BMI < 25 kg/m², moderate-to-high physical activity, a healthy diet, no smoking, and moderate alcohol consumption. We estimated odds ratios (OR) with 95% confidence intervals (CIs) adjusted for age, sex, education, and FHD.

RESULTS

Compared to a poor/moderate lifestyle, a healthy lifestyle was associated with a reduced risk of LADA (OR 0.51, CI 0.34-0.77) and type 2 diabetes (OR 0.09, CI 0.05-0.15). A healthy lifestyle conferred a reduced risk irrespective of FHD and high-risk HLA genotypes. Having a BMI < 25 kg/m² conferred the largest risk reduction for both LADA (OR 0.54, CI 0.43-0.66) and type 2 diabetes (OR 0.12, CI 0.10-0.15) out of the individual items.

CONCLUSION

People with a healthy lifestyle, especially a healthy body weight, have a reduced risk of LADA including those with genetic susceptibility to diabetes.

Association between 4-dimension lifestyle pattern and 10-year mortality risk in Chinese individuals older than 65: a population-based cohort study

While the impact of a 4-dimension lifestyle pattern (4DL) on older people's mortality (aged ≥65 years) has been reported in high-income countries, few studies investigated the association between lifestyle pattern and disease-accompanied mortality, or examined the difference among different age or gender groups in low- and middle-income countries. We followed up 16,954 Chinese older participants from 2008 to 2018 and adopted the Cox proportional hazard model to evaluate the protective effect of 4DL. After adjustment for confounders, individuals with 3-4 4DL scores had a 38% reduction in all-cause mortality risk, and up to 36%, 42% and 41% reduced risk of mortality accompanied by hypertension, respiratory disease and dementia, respectively in contrast with those scored 0. Compared with octogenarians, nonagenarians, and centenarians, adhering to 3-4 4DL could further reduce the mortality risks in the younger elderly (aged 65-79 years). This study shows that among the elderly population in China, participants who adhered to 4DL had a lower all-cause mortality risk than those who did not. Additionally, hypertension, respiratory disease, or dementia accompanied mortality risk was also reduced significantly. The findings indicated that the positive effects of 4DL on longevity should be acknowledged in China's older population, especially for the younger elderly.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Associations Between Healthy Lifestyle Trajectories and the Incidence of Cardiovascular Disease With All-Cause Mortality: A Large, Prospective, Chinese Cohort Study

Background: Lifestyles generally change across the life course yet no prospective study has examined direct associations between healthy lifestyle trajectories and subsequent cardiovascular disease (CVD) or all-cause mortality risk. Methods: Healthy lifestyle score trajectories during 2006-2007, 2008-2009, and 2010-2011 were collated through latent mixture modeling. An age-scale based Cox proportional hazard regression model was implemented to calculate hazard ratios (HR) with corresponding 95% confidence intervals (CI) for developing CVD or all-cause mortality across healthy lifestyle trajectories. Results: 52,248 participants were included with four distinct trajectories identified according to healthy lifestyle scores over 6 years i.e., low-stable (n = 11,248), high-decreasing (n = 7,374), low-increasing (n = 7,828), and high-stable (n = 25,799). Compared with the low-stable trajectory, the high-stable trajectory negatively correlated with lower subsequent risk of developing CVD (HR, 0.73; 95% CI, 0.65-0.81), especially stroke (HR, 0.70; 95% CI, 0.62-0.79), and all-cause mortality (HR, 0.89; 95% CI, 0.80-0.99) under a multivariable-adjusted model. A protective effect for CVD events was observed only in men and in those without diabetes, while a reduced risk of all-cause mortality was observed only in those older than 60 years, though interactions were not statistically significant. Marginally significant interactions were observed between the changing body mass index (BMI) group, healthy lifestyle score trajectories and stratified analysis. This highlighted an inverse correlation between the high-stable trajectory and CVD in BMI decreased and stable participants as well as all-cause mortality in the stable BMI group. The low-increasing trajectory also had reduced risk of CVD only when BMI decreased and in all-cause mortality only when BMI was stable. Conclusions: Maintaining a healthy lifestyle over 6 years corresponds with a 27% lower risk of CVD and an 11% lower risk in all-cause mortality, compared with those engaging in a consistently unhealthy lifestyle. The benefit of improving lifestyle could be gained only after BMI change is considered further. This study provides further evidence from China around maintaining/improving healthy lifestyles to prevent CVD and early death.

[Adherence to healthy lifestyle behaviours in patients with type 2 diabetes in Spain]

OBJECTIVES

The aim of this study was to report on the main lifestyle components and related factors in adults with diabetes type 2 treated in Primary Care clinics in Spain.

MATERIAL AND METHODS

A cross-sectional and multicentre study was performed on a consecutive sample of patients with type 2 diabetes attending 25 Primary Care clinics between April 2018 and April 2019. Data were collected by auditing the computerised medical records, and an interview. An analysis was carried out on adherence to 4 healthy lifestyle trends (Mediterranean diet, regular exercise, not smoking, and emotional well-being).

RESULTS

A total of 412 patients were included in the analysis (mean age 69 (SD 8.65) years; 50.2% men). Only a minority was highly adherent to the Mediterranean diet, 92 (22.3%). Regular physical activity was carried out by 189 (45.8%). A total of 361 (87.6%) were non-smoking, and 259 (62.8%) felt emotional well-being. A small number (9, 2.1%) of patients had not followed any of the healthy lifestyle recommendations, with 87 (21.1%) following one, 145 (35.1%) two, 128 (31%) three, and 43 (10.4%) all 4 healthy habits: diet, exercise, not smoking, and emotional well-being. Healthy lifestyle adherence was related to gender. Obesity is poorly associated with adherence to diet and physical activity. The results for age, time with the disease, socioeconomic status, and treatment regimen were not consistent.

CONCLUSIONS

This study suggest that adherence to a healthy lifestyle pattern in DM2 is low. Less than a quarter follow a healthy diet, and less than a half practice regular exercise. Gender is the variable that most influences a healthy lifestyle in DM2, but not age, time with the disease, or treatment regimen.