Age-related disparities in diabetes risk attributable to modifiable risk factor profiles in Chinese adults: a nationwide, population-based, cohort study

Abstract

Background National investigations of age-specific modifiable risk profiles for diabetes are crucial to promote personalised strategies for the prevention and control of diabetes, particularly in countries such as China, which is experiencing both a diabetes epidemic and a rapidly ageing population. We aimed to examine the associations of 12 potentially modifiable socioeconomic, lifestyle, and metabolic risk factors with diabetes in a nationwide prospective cohort of Chinese adults across four age groups. MethodsWe analysed data from the China Cardiometabolic Disease and Cancer Cohort Study, a nationwide, population-based, cohort study done between Jan 1, 2011, and Dec 31, 2016. Among 93 781 participants without diabetes at baseline and with complete information available about risk factors and diabetes incidence, we examined the hazard ratios (HRs) and population-attributable risk percentages (PAR%s) of incident diabetes associated with 12 potentially modifiable risk factors: two socioeconomic risk factors (less education and intermediate or low grade occupation), five lifestyle risk factors (unhealthy diet, physical inactivity, current alcohol consumption, current smoking, and unhealthy sleep), and five metabolic risk factors (general or central obesity, insulin resistance, prediabetes, hypertension, and dyslipidaemia) across four age groups (40 to <55 years, 55 to <65 years, 65 to <75 years, and ≥75 years).Findings With 337 932 person-years' follow-up, 6171 participants developed diabetes. Although diabetes risk attributable to metabolic risk factors decreased slightly with age, metabolic risk factors accounted for most incident diabetes cases across all age groups (from a PAR% of 73•8% in participants aged 40 to <55 years to 65•5% in those aged ≥75 years), with prediabetes (ranging from a PAR% of 56•6% to 47•4%, with increasing age) and hypertension (PAR% of 16•8% to 28•6% with increasing age) being the leading risk factors across all age groups. By contrast, diabetes risk attributable to lifestyle risk factors increased substantially with age (with a PAR% of 9•9% in adults aged 40 to <55 years increasing to 29•7% in adults aged ≥75 years), and was especially evident in participants aged 75 years or older, in whom unhealthy sleep was the largest lifestyle factor, accounting for a PAR% of 17•0%. The impact of socioeconomic risk factors on diabetes was mainly driven by less education, which contributed to a PAR% of 7•1% to 12•4% in participants younger than 75 years but conferred no excess risk in participants aged 75 years or older. The risk of diabetes associated with unhealthy sleep significantly increased with age group (HR 1•04 [95% CI 0•95-1•13] among participants aged 40 to <55 years vs 1•36 [1•01-1•85] among participants aged ≥75 years; p interaction =0•0054), but there was an age-related decrease in the risk of diabetes associated with obesity (1•35 [1•23-1•48] vs 0•99 [0•72-1•36]; p interaction =0•0003), prediabetes (3•00 [2•67-3•37] vs 2•18 [1•41-3•37]; p interaction =0•0014), and dyslipidaemia (1•23...

Comprehensive risk profiles of family history and lifestyle and metabolic risk factors in relation to diabetes: A prospective cohort study

“…When possessing the same number of risk category, adults younger than 55 years were at higher risk of diabetes than their older counterparts. Similar age‐related decrease in diabetes risk associated with multiple risk factors such as obesity and dyslipidemia were observed in a recent study of 93 781 Chinese adults aged ≥40 years 41 . Besides, a genome‐wide association study in the UK Biobank identified different genetic variations associated with age at diagnosis of diabetes, supporting the hypothesis that the pathogenesis of diabetes changes with age 42 …”

“…Similar age‐related decrease in diabetes risk associated with multiple risk factors such as obesity and dyslipidemia were observed in a recent study of 93 781 Chinese adults aged ≥40 years. 41 Besides, a genome‐wide association study in the UK Biobank identified different genetic variations associated with age at diagnosis of diabetes, supporting the hypothesis that the pathogenesis of diabetes changes with age. 42 …”

Follow‐up frequency and clinical outcomes in patients with type 2 diabetes: A prospective analysis based on multicenter real‐world data

“…We suggested that education level may affect the behavioral pattern of follow‐up—patients with higher education level (high school and above) may have better treatment compliance, resulting in better metabolic outcomes. Recently, our group revealed that less education (lower than high school) is one of the socioeconomic risk factors which contribute to the diabetes risk in adults 22 . This work with real‐world, large sample data extended previous research on the association between follow‐up frequency and glycemic control, and we evaluated more additional metabolic parameters.…”

“…Recently, our group revealed that less education (lower than high school) is one of the socioeconomic risk factors which contribute to the diabetes risk in adults. 22 This work with real‐world, large sample data extended previous research on the association between follow‐up frequency and glycemic control, and we evaluated more additional metabolic parameters. Our results demonstrate that HFF was associated with greater improvements in metabolic outcomes, including FBG, HbA1c, blood pressure, and cholesterol level in HFF participants.…”

Molecular networks underlying cannabinoid signaling in skeletal muscle plasticity

“…Metabolic health generally decreases with advancing age (e.g., due to aging per se and due to behavioral changes such as decreased physical activity), which is reflected by an increased risk of dyslipidemia and insulin insensitivity at higher age (X. Liu et al, 2018; T. Wang et al, 2021a). Hence, it can be hypothesized that cannabinoid signaling and CB 1 expression is responsive to ageing in a metabolic tissue such as skeletal muscle.…”