A Fit-Fat Index for Predicting Incident Diabetes in Apparently Healthy Men: A Prospective Cohort Study

Predicting Time to Diabetes Diagnosis Using Random Survival Forests

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder with increasing population incidence. However, T2DM takes years to develop, allowing onset prediction and prevention to be a clinically effective treatment strategy. In this study we propose and assess a novel approach to diabetes prediction which integrates a specialized extension of the random forest algorithm known as random survival forest (RSF). Rather than predicting a binary outcome, this machine learning model incorporates survival analysis methodology to predict the time until a patient will receive a diabetes diagnosis if their current lifestyle is maintained. We trained a baseline model on 7,704 electronic medical records from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with 14 biomarker and comorbidity features across different measurement dates. Although tuning parameters were purposefully chosen for quick training rather than for predictive performance, our model exceeded expectations with a concordance index of 0.84. Thus, RSF models have been shown to produce accurate timelines of diabetes onset trajectory, providing patients with quantifiable and relatable risks that are easy to understand. The results of our study have substantial implications for advancing machine learning in clinical decision support and patient outcome predictions, emphasizing the role of innovative models in improving predictive accuracy.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

The impact of individualised versus standardised endurance and resistance training on the fitness-fatness index in inactive adults

OBJECTIVES

The aim of the current study was to investigate the impact of individualised versus standardised combined endurance and resistance training on the fitness-fatness index in physically inactive adults.

DESIGN

Randomised controlled trial.

METHODS

Fifty-four participants aged 21-55 years were randomised into three groups; 1) non-exercise control (n = 18), 2) standardised moderate-intensity continuous training (n = 18), or 3) individualised moderate-intensity continuous training + high-intensity interval training (n = 18). The fitness-fatness index was calculated by dividing cardiorespiratory fitness (expressed as metabolic equivalents) by the waist-to-height ratio. Participants were classified as likely responders to the intervention if a change of ≥1 fitness-fatness index unit was achieved.

RESULTS

The individualised group showed the greatest fitness-fatness index improvement (between group difference p < 0.001), with 100 % of this group classified as likely responders, compared to the standardised (68 %) and non-exercise control (0 %) groups.

CONCLUSIONS

An individualised, threshold-based exercise programme may produce more favourable changes in the fitness-fatness index than a standardised exercise programme.

Fit-Fat Index is better associated with heart rate variability compared to fitness and fatness alone as indicators of cardiometabolic human health

OBJECTIVES

Cardiorespiratory fitness and fatness indicators have been related to heart rate variability (HRV) parameters. The Fit-Fat Index (FFI) is a single index combining cardiorespiratory fitness and fatness indicators. To the best of our knowledge, no studies have previously analyzed whether FFI are related to cardiac autonomic nervous system activity assessed through HRV parameters. This study aimed (i) to examine the association of cardiorespiratory fitness, fatness indicators, and FFI with HRV parameters; and (ii) to report what of the different fatness indicators included in FFI is better associated with HRV parameters in sedentary adults.

METHODS

One hundred and fifty healthy adults (74 women; 76 men), aged between 18 and 65 years old, participated in this cross-sectional study. We measured cardiorespiratory fitness (maximal oxygen consumption) and fatness indicators (waist-to-height ratio [WHR], fat mass percentage [FM%] and visceral adipose tissue [VAT]). Three FFIs were calculated as the quotient between cardiorespiratory fitness and one out of three possible fatness indicators: Fit-Fat Index calculated waist-to-height ratio (FFIWHR ), Fit-Fat Index calculated with FM% (FFIFM% ), and Fit-Fat Index calculated with VAT (FFIVAT ). HRV parameters were measured in resting conditions using a Polar RS800CX.

RESULTS

FFIWTHR , FFIFM% and FFIVAT were related to different HRV parameters (β ranges between -0.507 and 0.529; R2 ranges between 0.096 and 0.275; all p < .001) and the association was stronger with HRV parameters than the isolated fitness or fatness indicators (β ranges between -0.483 and 0.518; R2 ranges between 0.071 and 0.263; all p < .001). FFIVAT was the index more consistently associated with HRV parameters (β ranges between -0.507 and 0.529; R2 ranges between 0.235 and 0.275; all p < .001).

CONCLUSION

Our study suggests that compound FFIs are better predictors of HRV parameters than either cardiorespiratory fitness or fatness indicators alone. The FFIVAT was the best index in terms of its association to HRV.

Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression

Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 497 patients with stage 3-5 chronic kidney disease (CKD) who were treated at the ward of Taipei Veterans General Hospital from January 2006 to 2019 in Taiwan. The patients underwent 3-year-long follow-up sessions for clinical measurements, which occurred every 3 months. Three time-dependent survival models, namely the Cox proportional hazard model (Cox PHM), random survival forest (RSF), and an artificial neural network (ANN), were used to process patient demographics and laboratory data for predicting progression to renal failure, and important features for optimal prediction were evaluated. The individual prediction of CKD progression was validated using the Kaplan-Meier estimation method, based on patients' true outcomes during and beyond the study period. The results showed that the average concordance indexes for the cross-validation of the Cox PHM, ANN, and RSF models were 0.71, 0.72, and 0.89, respectively. RSF had the best predictive performances for CKD patients within the 3 years of follow-up sessions, with a sensitivity of 0.79 and specificity of 0.88. Creatinine, age, estimated glomerular filtration rate, and urine protein to creatinine ratio were useful factors for predicting the progression of CKD patients in the RSF model. These results may be helpful for instantaneous risk prediction at each follow-up session for CKD patients.

Prognostic value of exercise capacity in incident diabetes: a country with high prevalence of diabetes

BACKGROUND

Diabetes Mellitus (DM) is a fast-growing health problem that imposes an enormous economic burden. Several studies demonstrated the association between physical inactivity and predicting the incidence of diabetes. However, these prediction models have limited validation locally. Therefore, we aim to explore the predictive value of exercise capacity in the incidence of diabetes within a high diabetes prevalence population.

METHODOLOGY

A retrospective cohort study including consecutive patients free of diabetes who underwent clinically indicated treadmill stress testing. Diabetic patients at baseline or patients younger than 18 years of age were excluded. Incident diabetes was defined as an established clinical diagnosis post-exercise testing date. The predictive value of exercise capacity was examined using Harrell's c-index, net reclassification index (NRI), and integrated discrimination index (IDI).

RESULTS

A total of 8,722 participants (mean age 46 ± 12 years, 66.3% were men) were free of diabetes at baseline. Over a median follow-up period of 5.24 (2.17-8.78) years, there were 2,280 (≈ 26%) new cases of diabetes. In a multivariate model adjusted for conventional risk factors, we found a 12% reduction in the risk of incident diabetes for each METs achieved (HR, 0.9; 95% CI, 0.88-0.92; P < 0.001). Using Cox regression, exercise capacity improved the prediction ability beyond the conventional risk factors (AUC = 0.62 to 0.66 and c-index = 0.62 to 0.68).

CONCLUSION

Exercise capacity improved the overall predictability of diabetes. Patients with reduced exercise capacity are at high risk for developing incidence diabetes. Improvement of both physical activity and functional capacity represents a preventive measure for the general population.

Changes in Fitness-Fatness Index following a Personalized, Community-Based Exercise Program in Physically Inactive Adults: A Randomised Controlled Trial

Fitness-fatness index (FFI) is used to identify those at high risk of developing type 2 diabetes and cardiovascular events. It is measured as the ratio between an individual's cardiorespiratory fitness (CRF) and waist-to-height ratio. Studies suggest that CRF and waist-to-height ratio are modifiable and can be improved by exercise. However, there is limited evidence surrounding a personalized approach to exercise prescription. This study investigated the impact of a 12-week personalized exercise program on FFI among sedentary individuals. It was hypothesized that the intervention would be effective in improving FFI in this cohort. One hundred and forty-two participants were randomized into two groups: i) personalised community-based intervention (n = 70); or ii) control (n = 72). Both groups underwent baseline anthropometric testing and a submaximal 'talk-test' to determine individual exercise intensities and baseline FFI. During the intervention, the control group underwent normal activities, whilst the treatment group received a 12-week personalised exercise program based on the American Council on Exercise (ACE) Integrated Fitness Training (IFT) guidelines. After 12-weeks, the treatment group demonstrated a significant increase in FFI (+13%), whilst the control group (-2%) showed a slight decrease (between-group difference, p = < 0.001). Both CRF (+12%) and waist-to-height (-2%) also showed significant favourable changes in the treatment group, with no change in the control group (between group difference, p = 0.01). These findings indicate that a personalised approach to exercise prescription using the ACE IFT guidelines are beneficial in reducing FFI. Consequently, FFI could be implemented within standardized approaches to exercise to help reduce the risk of developing chronic conditions.

The Effects of Standardised versus Individualised Aerobic Exercise Prescription on Fitness-Fatness Index in Sedentary Adults: A Randomised Controlled Trial

A poor Fitness Fatness Index (FFI) is associated with type 2 diabetes incidence, other chronic conditions (Alzheimer's, cancer, and cardiovascular disease) and all-cause mortality. Recent investigations have proposed that an individualised exercise prescription based on ventilatory thresholds is more effective than a standardised prescription in improving cardiorespiratory fitness (CRF), a key mediator of FFI. Thus, the aim of the current study was to determine the effectiveness of individualised versus standardised exercise prescription on FFI in sedentary adults. Thirty-eight sedentary individuals were randomised to 12-weeks of: (1) individualised exercise training using ventilatory thresholds (n = 19) or (2) standardised exercise training using a percentage of heart rate reserve (n = 19). A convenience sample was also recruited as a control group (n=8). Participants completed CRF exercise training three days per week, for 12-weeks on a motorised treadmill. FFI was calculated as CRF in metabolic equivalents (METs), divided by fatness determined by waist to height ratio (WtHR). A graded exercise test was used to measure CRF, and anthropometric measures (height and waist circumference) were assessed to ascertain WtHR. There was a difference in FFI change between study groups, whilst controlling for baseline FFI, F (2, 42) = 19.382 p < .001, partial η2 = 0.480. The magnitude of FFI increase from baseline was significantly higher in the individualised (+15%) compared to the standardised (+10%) (p = 0.028) and control group (+4%) (p = <.001). The main finding of the present study is that individualised exercise prescription had the greatest effect on improving FFI in sedentary adults compared to a standardised prescription. Therefore, an individualised based exercise prescription should be considered a viable and practical method of improving FFI in sedentary adults.

Changes in the Fitness Fatness Index following reduced exertion high-intensity interval training versus moderate-intensity continuous training in physically inactive adults

Background

Many adults do not reach the recommended exercise participation guidelines, often citing lack of time as a barrier. Reduced exertion high-intensity training (REHIT) is a mode of exercise that takes as few as 10 min and has been shown to be as effective as other modalities. The Fitness Fatness Index (FFI) is a recently developed index that is used to predict cardiovascular disease (CVD) risk. The aim of this study was to determine the efficacy of a REHIT vs. a traditional moderate-intensity continuous training (MICT) on FFI in physically inactive adults.

Methods

Thirty-two participants were randomized into one of two 8-week exercise intervention groups: (i) REHIT (n = 16); (ii) MICT (n = 16). The REHIT group performed 10 min of individualized cycling intervals on 2–4 days of the week. The MICT group were prescribed aerobic exercise at 50–65% of their heart rate reserve (HRR) on 3–5 days of the week. FFI was recorded at baseline and post 8-weeks, with FFI being calculated as cardiorespiratory fitness (CRF) (expressed as metabolic equivalents) divided by waist to height ratio (WtHR). A 1-unit increase in FFI was recognized as a clinically significant change in FFI.

Results

The REHIT group showed significantly greater (+1.95, ±0.63) improvements in FFI compared to those in the MICT (+0.99, ±0.47) group (between group difference, p &lt; 0.001). Furthermore, there was a greater proportion of participants who achieved a clinically significant change in FFI in the REHIT group (12/12, 100%) than in the MICT group (8/15, 53%) (between group difference, p = 0.01).

Conclusion

This study suggests that REHIT may be a more efficacious exercise modality to increase FFI than MICT. This outcome is beneficial as the clinician can prescribe REHIT to physically inactive adults who cite lack of time as a barrier to physical activity participation and achieve significant reductions in CVD risk.

Independent and joint associations of non-exercise cardiorespiratory fitness and obesity with risk of type 2 diabetes mellitus in the Rural Chinese Cohort Study

BACKGROUND AND AIMS

An association between cardiorespiratory fitness (CRF) and type 2 diabetes mellitus (T2DM) has not been established in the Chinese population. This study aimed to estimate the independent and joint associations of CRF and obesity with T2DM incidence in the rural Chinese population.

METHODS AND RESULTS

We conducted a prospective study of 11,825 non-T2DM subjects among rural Chinese adults. Cox regression models were used to estimate the independent and joint associations between CRF and obesity exposure on T2DM. Restricted cubic splines were used to model the dose-response association. During a median follow-up of 6.01 years, 835 participants developed T2DM. In comparison to quartile 1 of CRF, the multivariate hazard ratios (HRs) and 95% confidence intervals (CIs) of quartiles 2, 3, 4 were 0.75 (0.61-0.91), 0.54 (0.43-0.68), and 0.42 (0.32-0.55), respectively. When stratified by sex, the results were similar. Joint analyses showed that overweight/obesity-unfit individuals had a 2.28 times higher risk of developing T2DM than the normal weight-fit referent (HR 2.28, 95% CI 1.84-2.83; P_interaction <0.001). The risk for the overweight/obesity-fit category (HR 1.61, 95% CI 1.21-2.15) was larger than for the normal weight-unfit category (HR 1.38, 95% CI 0.97-1.95) versus the normal weight-fit referent. Similar joint associations for waist circumference and CRF with T2DM were also observed.

CONCLUSION

A negative association was observed between CRF and risk of T2DM. Overweight/obese or abdominal obesity and unfit participants showed the highest risks of T2DM. It is therefore strongly recommended that fitness-enhancing be encouraged for the prevention of T2DM, especially among obesity participants.

Exercise Training Intensity and the Fitness-Fatness Index in Adults with Metabolic Syndrome: A Randomized Trial

Background

Cardiorespiratory fitness and fatness (notably central obesity) are mediating factors of the metabolic syndrome (MetS) and consequent cardiovascular disease (CVD)/mortality risk. The fitness-fatness index (FFI) combines these factors and has been reported to be a better indicator of CVD and all-cause mortality risk, beyond the capacity of either fitness or fatness alone.

Objective

This study sought to investigate the effects of different exercise intensities on FFI in adults with MetS.

Methods

This was a sub-study of the ‘Exercise in the prevention of Metabolic Syndrome’ (EX-MET) multicentre trial. Ninety-nine adults diagnosed with MetS according to the International Diabetes Federation criteria were randomized to one of the following 16-week exercise interventions: i) moderate-intensity continuous training (MICT) at 60–70% HRpeak for 30 min/session (n = 34, 150 min/week); ii) 4 × 4 min bouts of high-intensity interval training at 85–95% HRpeak, interspersed with 3-min active recovery at 50–70% HRpeak (n = 34, 38 min/session, 114 min/week); and iii) 1 × 4 min bout of HIIT at 85–95% HRpeak (n = 31, 17 min/session, 51 min/week). Cardiorespiratory fitness (peak oxygen uptake, V̇O2peak) was determined via indirect calorimetry during maximal exercise testing and fatness was the ratio of waist circumference-to-height (WtHR). FFI was calculated as V̇O2peak in metabolic equivalents (METs) divided by WtHR. A clinically meaningful response to the exercise intervention was taken as a 1 FFI unit increase.

Results

Seventy-seven participants completed pre and post testing to determine FFI. While there was no significant between group difference (p = 0.30), there was a small group x time interaction effect on FFI [F(2, 73) = 1.226; η2 = 0.01], with numerically greater improvements following HIIT (4HIIT, + 16%; 1HIIT, + 11%) relative to MICT (+ 7%). There was a greater proportion of participants who had a clinically meaningful change in FFI following high-volume HIIT (60%, 15/25) and low-volume HIIT (65%, 17/26) compared to MICT (38%, 10/26), but with no significant between-group difference (p = 0.12). A similar trend was found when a sub-analysis comparing the FFI between those with type 2 diabetes (MICT, 33%, 3/9; high-volume HIIT, 64%, 7/11; and low-volume HIIT, 58%, 7/12) and without type 2 diabetes (MICT, 41%, 7/17; high-volume HIIT, 57%, 8/14; low-volume HIIT, 71%, 10/14).

Conclusion

Although there were no statistically significant differences detected between groups, this study suggests that the response to changes in FFI in adults with MetS may be affected by exercise intensity, when numerical differences between exercise groups are considered. Further research is warranted.

Trial registration number and date of registration: ClinicalTrials.gov NCT01676870; 31/08/2012.

Analysis of fitness among Korean adults by the cause-effect relation in lump mean scheme

BACKGROUND

The aim of this study was to examine whether the fitness of Korean adults can be analyzed by the cause-effect relation using the linearity or Gaussianity in the lump mean scheme (LMS).

METHODS

This study analyzed previous results for the sit-up test obtained in the LMS by regression analysis in Sigmaplot 14. The effects of the body mass index (BMI) and new waist-to-height ratio (WHT2R) introduced by the present author on fitness were investigated.

RESULTS

The distribution of the sit-up test score with respect to the BMI and WHT2R were interpreted by their Gaussianity and linearity, respectively. This means that the muscular endurance of males is determined by two causes (fat and muscle) when the BMI is a variable and one cause (abdominal fat) when the WHT2R is a variable.

CONCLUSIONS

Personal exercise aims were simpler to establish using WHT2R than using BMI. On the other hand, it was recommended for people with a low BMI to increase their fitness using exercises that increase their muscle mass.

Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death

INTRODUCTION

The present study evaluated the application of incorporating non-linear J/U-shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non-AMI-related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus.

METHODS

This was a territory-wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti-diabetic agents between January 1st, 2009 to December 31st, 2009 at government-funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non-AMI-related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model.

RESULTS

This study included 261 308 patients (age = 66.0 ± 11.8 years old, male = 47.6%, follow-up duration = 3552 ± 1201 days, diabetes duration = 4.77 ± 2.29 years). Mean HbA1c and low high-density lipoprotein-cholesterol (HDL-C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL-C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J-shaped relationship with non-AMI-related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI) = [0.662, 0.669]) and 0.677 (95% CI = [0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models.

CONCLUSION

A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.

The association of fitness and fatness with intermediate hyperglycemia incidence in women: A cohort study

The purpose of this study was to examine the associations of cardiorespiratory fitness, general adiposity, and central adiposity with incident intermediate hyperglycemia (IH) in women. We conducted a prospective cohort study of 1534 women aged 20-79 years old who had an annual health check-up with no history of major chronic diseases. At baseline, fitness was assessed by a Balke graded exercise test, and the estimated metabolic equivalents were used to create quartile groups. Women were also grouped based on their body mass index (<25 kg/m², 25-29.9 kg/m², and ≥ 30 kg/m²) and waist-to-height ratio (≥0.50 or < 0.50). Cox proportional hazards models were conducted to assess the association of fitness and fatness variables with incident IH defined as fasting glucose of 5.6-6.9 mmol/L. Overall, 18.1% (n = 277) of the women developed IH during an average follow-up of 5.06 years. Fitness, body mass index, and waist-to-height ratio at baseline were the independent predictors of the IH incidence in separate age-adjusted models; yet when all three variables were included in the same model along with confounding variables, only fitness remained significant and demonstrated a clear inverse association with incident IH (P-for-trend <0.001). Health promotion efforts should focus on improving fitness for the prevention of IH in women.

Factors affecting the survival of patients with colorectal cancer using random survival forest

PURPOSE

Colorectal cancer is one of the most common cancers and the leading cause of cancer death in Iran. This study aimed to develop and validate a random survival forest (RSF) to identify important risk factors on mortality in colorectal patients based on their demographic and clinical-related variables.

METHODS

In this retrospective cohort study, the information of 317 patients with colorectal cancer who were referred to Imam Khomeini Clinic of Hamadan during the years of 2002 to 2017 were examined. Patient survival was calculated from the time of diagnosis to death. In the present study, the RSF model was used to identify factors affecting patient survival. Also, the results of the RSF model were compared with the Cox model. The data were analyzed using R software (version 3.6.1) and survival packages.

RESULTS

One-, 2-, 3-, 4-, 5-, and 10-year survival rates of included patients were 81.4%, 63%, 57%, 52%, 45%, and 34%, respectively, and the median survival was obtained to be 53 months. The number of 150 patients was died at this time period. The four most important predictors of survival included metastasis to other organs, WBC count, disease stage, and number of lymphomas involved. RSF method predicted survival better than the conventional Cox proportional hazard model.

CONCLUSION

We found that metastasis to other organs, WBC count, disease stage, and number of lymphomas involved were the most four most important predictors of low survival for colorectal cancer patients.

Association Between Cardiorespiratory Fitness and Risk of Type 2 Diabetes: A Meta-Analysis

OBJECTIVE

This meta-analysis aimed to (1) quantify the association of cardiorespiratory fitness (CRF) with type 2 diabetes risk in the general population and statin users and (2) investigate the joint effects of CRF and fatness with type 2 diabetes risk.

METHODS

Databases were searched for cohort studies reporting the association between CRF and type 2 diabetes risk. Summary hazard ratios (HRs) were obtained using random-effects models.

RESULTS

Fifteen studies were included. The HRs of type 2 diabetes for every 1-metabolic equivalent increase in CRF were 0.90 (95% CI: 0.86-0.94) for the general population and 0.92 (95% CI: 0.87-0.97) for statin users, and the HRs were linearly shaped (both P_nonlinearity  > 0.40). Compared with the nonstatin cohort, there was an increased risk of type 2 diabetes in statin users with the lowest and moderate CRF categories, but this was not present in the highest CRF category. The HR of type 2 diabetes for overweight/obesity-fit category versus normal weight-fit category was larger than that of the normal weight-unfit category versus the normal weight-fit category (P_interaction   = 0.004).

CONCLUSIONS

There was an inverse and dose-dependent association between CRF and type 2 diabetes risk. High CRF may eliminate the diabetogenic effect from statins, yet decreased body weight index seems superior in preventing type 2 diabetes.

The Association of Fit-Fat Index with Incident Diabetes in Japanese Men: A Prospective Cohort Study

Type 2 diabetes is increasing globally and in Asia. The purpose of this study was to examine the association of a fit-fat index (FFI) with diabetes incidence among Japanese men. In total 5,014 men aged 18–64 years old, who had an annual health check up with no history of major chronic disease at baseline from 2002 to 2009 were observed. CRF was estimated via cycle ergometry. Overall, 7.6% of the men developed diabetes. The mean follow-up period was 5.3 years. Hazard ratios, 95% confidence intervals and P trend for diabetes incidence were obtained using the Cox proportional hazards model while adjusting for confounding variables. High FFI demonstrated lower risk 0.54 (0.36–0.82) compared to low BMI 0.63 (0.44–0.90), low WHtR 0.64 (0.41–1.02), and High CRF 0.72 (0.51–1.03). FFI showed a marginally stronger dose response relationship across quartiles (P (trend) =0.001) compared to BMI (P (trend) =0.002), WHtR (P (trend) =0.055), and CRF (P (trend) =0.005). Overall, both fitness and fatness play independent roles in determining diabetes incidence in Japanese men. FFI may be a more advantageous physical fitness measure because it can account for changes in fitness and/or fatness.

Cardiorespiratory fitness, fatness and incident diabetes

AIMS

Increases in prevalence have led to a diabetes pandemic. Obesity and low cardiorespiratory fitness (CRF) are considered to be central mechanisms. We investigated if the effect of CRF on diabetes risk was equivalent across levels of fatness among healthy men.

METHODS

In total 4988 middle-aged Caucasian employed men free of cardiovascular disease, diabetes and cancer were included from the Copenhagen Male Study starting in 1970-71. CRF was assessed using a sub-maximal bicycle ergometer test and body mass index (BMI) was measured by height and weight. Their interaction and stratified associations with diabetes incidence were estimated in multivariable Cox-models including conventional risk factors and social class. Diabetes incidence was assessed through a national register.

RESULTS

During 44 years of follow-up, 518 (10.4%) incident cases of diabetes occurred. In the multi-adjusted model, the obese had a significantly higher risk of diabetes compared to normal weight men (Hazard Ratio (HR):4.89; 95% CI: 3.62-6.61) and CRF was significantly inversely associated with diabetes (HR:0.86; 95% CI: 0.75-0.98 per 10-unit increase in ml/kg/min¹ CRF). A significant multi-adjusted interaction between CRF, BMI and diabetes was found (p=0.009). The stratified multi-adjusted analyses on BMI showed a significantly stronger reduced risk of diabetes per 10-unit increase in ml/kg/min¹ of CRF among the obese (HR:0.58; CI: 0.38-0.89), but a weaker association among overweight (HR:0.86; CI: 0.71-1.03) and normal weight (HR:0.97; CI: 0.76-1.23).

CONCLUSION

High CRF has a stronger protective effect on diabetes among obese than among normal weight men, supporting the recommendation of fitness-enhancing physical activity for preventing diabetes among the obese.

Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79–0.80 and 0.43–0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

Fitness Fatness Index and Alzheimer-specific mortality

OBJECTIVE

We evaluated the specific association between a Fitness Fat Index (FFI) and Alzheimer's-specific mortality among a national sample of the broader U.S. adult population.

METHODS

FFI was calculated as cardiorespiratory fitness (CRF) divided by waist-to-height ratio (WHR). Data from the 1999-2006 National Health and Nutrition Examination Survey (NHANES) were used to identify 16,146 participants, ages 20-85. Data from participants in these cycles were linked to death certificate data from the National Death Index. Person-months of follow-up were calculated from the date of the interview until date of death or censoring on December 31, 2011, whichever came first.

RESULTS

In a Cox proportional hazard model, for every 1 FFI unit increase, participants had a 14% reduced hazard of Alzheimer-specific death (HR=0.86; 95% CI: 0.83-0.90; P<0.001). When including diabetes and hypertension (via physician-diagnosis) as covariates, results were unchanged (HR=0.87; 95% CI: 0.82-0.91; P<0.001). Results were also unchanged when restricting the sample to those 50+years (HR=0.92; 95% CI: 0.88-0.97; P=0.005) or stratifying by men (HR=0.85; 95% CI: 0.81-0.91; P<0.001) or women (HR=0.86; 95% CI: 0.79-0.94; P=0.002).

CONCLUSION

In this national sample of individuals at risk for Alzheimer's disease, increased FFI was associated with reduced risk of Alzheimer's-specific death. Thus, a more favorable fitness-to-fatness ratio is associated with reduced risk of Alzheimer's-specific mortality, underscoring the importance of fitness promoting and fatness reducing strategies.

The protective effects of a novel fitness-fatness index on all-cause mortality among adults with cardiovascular disease

BACKGROUND

We evaluated the specific association between a fitness-fatness index (FFI) and all-cause mortality among a national sample of US adults with coronary artery disease, congestive heart failure, or myocardial infarction. This FFI has recently emerged in the literature as a novel index of health.

HYPOTHESIS

We hypothesize that FFI will be inversely associated with mortality risk.

METHODS

The FFI was calculated as cardiorespiratory fitness divided by waist-to-height ratio. Data from the 1999-2006 National Health and Nutrition Examination Survey were used to identify 1206 participants, ages 20 to 85. Person-months of follow-up were calculated from the date of interview until date of death or censoring on December 31, 2011, whichever came first.

RESULTS

In a Cox proportional hazards model, for every 1-FFI-unit increase, participants had a 6% reduced all-cause mortality rate (hazard ratio [HR]: 0.94, 95% confidence interval [CI]: 0.91-0.97, P = 0.001; N = 1206). Results were similar among those diagnosed with coronary artery disease (HR: 0.94, 95% CI: 0.90-0.98, P = 0.007), congestive heart failure (HR: 0.95, 95% CI: 0.91-0.99, P = 0.02), or myocardial infarction (HR: 0.96, 95% CI: 0.92-0.99, P = 0.04). When examined in isolation, only fitness (and not fatness) was linked with survival benefits.

CONCLUSIONS

In this national sample, increased FFI was associated with reduced risk of all-cause mortality; this association was driven by the beneficial effects of fitness. This underscores the importance of tailored cardiac rehabilitation programs designed to promote fitness, in particular, among cardiac populations.