Diabetes in ageing: pathways for developing the evidence base for clinical guidance

Zhao

The Lancet Healthy Longevity

et al. 2021

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...

Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

The Lancet Healthy Longevity

et al. 2021

Diabetes Research and Clinical Practice

“…One could claim that diabetic patients migrate between clusters with long duration of diabetes [45]. However, it is considered that individuals are phenotyped before the onset of diabetes and do not migrate between clusters, even with the progression of disease duration [15,46].…”

Section: Sub-classification Of Adult-onset Diabetes Mellitus Based On Cluster Analysismentioning

confidence: 99%

“…Glycaemic control is good, and there were few microangiopathies. In elderly MARD individuals, attention should be paid to senile syndromes such as frailty syndrome, mild cognitive impairment, osteoporosis and fracture, and ASCVD [45].…”

Section: Cluster 5: Mardmentioning

confidence: 99%

Novel strategies for glycaemic control and preventing diabetic complications applying the clustering-based classification of adult-onset diabetes mellitus: A perspective

Tanabe

Masuzaki

Shimabukuro

2021

Systems-Aligned Precision Medicine—Building an Evidence Base for Individuals Within Complex Systems

2022

Advances in data collection and storage, computing, and analytics, combined with a growing recognition of the clinical importance of heterogeneity among individuals, are producing new solutions to clinical problems and a paradigmatic shift in evidence generation: precision medicine.The tagline for precision medicine-right treatment, right patient, right time-distills the operating principle of tailoring treatment decisions to individuals' unique characteristics and evolving health. [1][2][3] While this goal is not new, what is novel is the growing capacity of analytic tools to generate datadriven strategies for increasing the precision of prevention and care. 2,4 Put otherwise, the engine of empirical precision medicine is advanced analytics, and the fuel is patient-level data.A statistical approach to precision medicine uses patient-level data and formalizes clinical decision-making into a model composed of patients' features, key decision points, treatment options at those decision points, and primary outcomes to be optimized. What is remarkable about statistical precision medicine algorithms, known as optimal dynamic treatment regimes, 3 is how they generate knowledge from data. Rather than relying on known or hypothesized relationships between interventions, health states, and outcomes, statistical and machine learning analytics can learn directly from the data how to tailor treatments optimally. The translational vision of precision medicine is to integrate optimal treatment recommendations into point-of-care tools that can offer real-time, tailored decision support.Yet the question of what treatment works best for whom, to some degree, relies on broad and multilevel contextual factors. The clinical decision-making that happens as part of routine care, rather than in a research setting, is shaped by complexity at nearly every stage. Patient preferences, digitized workflows, billing and payment practices, and labor shortages can affect clinical decisions,