Background Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. Objective We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. Methods We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. Results Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001). Conclusions A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.

BackgroundLong-term cancer, cardiovascular disease, diabetes and osteoarthritis may increase the risk of mental disorders, but which was more harmful and whether the associations differed between genders is unclear.MethodsWe included 115,094 participants (54.3% women) aged 45–64 years from the 45 and Up Study who were free of depression, anxiety, and Parkinson's disease at baseline (2006–2009). The incidence of depression and anxiety was identified using claim databases during follow-up until December 2016. Cox regression models were used to examine the association of cancer, cardiovascular disease, diabetes, and osteoarthritis at baseline with incident depression and anxiety.FindingsDuring a mean eight-year follow-up (958,785 person-year), the cumulative incidence of depression and anxiety was 12.5% and 5.9% in the healthy population. Hazard ratios ([HRs] (95% CI) versus healthy population) for incident depression associated with long-term cancer, cardiovascular disease, diabetes, and osteoarthritis were 1.19 (95% CI: 1.13–1.25), 1.08 (1.00–1.16)), 1.18 (1.09–1.28), and 1.94 (1.80–2.10), respectively. The corresponding HRs (95% CIs) for incident anxiety were 1.11 (1.03–1.20), 1.26 (1.14–1.39), 1.10 (0.98–1.24), and 2.01 (1.80–2.23), respectively. The positive association between cancer and incident depression was more evident in men (HR (95% CI): 1.24 (1.13–1.35) than in women (1.14 (1.07–1.21). Long-term diabetes was an independent risk factor for incident anxiety in men (1.21 (1.02–1.44) but not in women (1.09 (0.93–1.28)).InterpretationLong-term osteoarthritis, cardiovascular disease, and cancer were independent risk factors for incident depression and anxiety in both genders with osteoarthritis having the highest relative risk.

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Background: Determining the incidence, progression, and patterns of multimorbidity are important for the prevention, management, and treatment of concurrence of multiple conditions. This study aimed to analyze major multimorbidity patterns and the association of the onset of a primary condition or combinations of a primary and a secondary condition with the progression to subsequent conditions. Methods: We included 53,867 participants aged 45-64 years from the 45 and Up Study who were free of 10 predefined chronic conditions at baseline (2006-2009). The incidence of multimorbidity (coexistence of ≥2, ≥3, and ≥4 conditions) was identified using the claims database until December 31, 2016. The primary, secondary, tertiary, and quaternary condition for each participant was defined according to its temporal order of onset. Results: During a mean 9-years follow-up, the cumulative incidence of primary, secondary, tertiary, and quaternary conditions was 49.6, 23.7, 9.0, and 2.9%, respectively. The time to develop a subsequent condition decreased with the accumulation of conditions (P < 0.0001). Two concurrent cardiometabolic disorders (CMDs, 30.4%) and CMDs clustered with musculoskeletal disorders (15.2%), mental disorders (13.5%), asthma (12.0%), or cancer (8.7%) were the five most common multimorbidity patterns. CMDs tended to occur prior to mental or musculoskeletal disorders but after the onset of cancers or asthma. Compared with all participants who developed cancer as a primary condition, individuals who experienced mental disorders/neurodegenerative disorders and a comorbidity as cardiovascular disease, hypertension, dyslipidemia, diabetes, asthma, or osteoarthritis were 3.36-10.87 times more likely to develop cancer as a tertiary condition. Individuals with neurodegenerative disorders and a comorbidity as hypertension, dyslipidemia, osteoarthritis, or asthma were 5.14-14.15 times more likely to develop mental disorders as a tertiary condition. Shang et al. Incidence, Progression, Patterns of Multimorbidity Conclusions: A high incidence of multimorbidity in middle-aged adults was observed and CMDs were most commonly seen in multimorbidity patterns. There may be accelerated aging after a primary condition occurs. Our findings also reveal a potential preventative window to obviate the development of secondary or tertiary conditions.

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