Association of the systemic inflammation and anthropometric measurements with cancer risk: a prospective study in MJ cohort.

Objective: To investigate the specific role of inflammation in the connection between obesity and the overall incidence of cancer. Methods: A total of 356,554 participants in MJ cohort study were included. Systemic inflammation markers from blood samples and anthropometric measurements were determined using professional instruments. The Cox model was adopted to evaluate the association. Results: Over a median follow-up of 8.2 years, 9,048 cancer cases were identified. For individual systemic inflammation biomarkers, the overall cancer risk significantly escalated as blood C-reactive protein (CRP) (hazard ratio (HR)=1.036 (1.017-1.054)) and globulin (GLO) (HR=1.128 (1.105-1.152)) levels increased, and as hemoglobin (HEMO) (HR=0.863 (0.842-0.884)), albumin (ALB) (HR=0.846 (0.829-0.863)) and platelets (PLA) (HR=0.842 (0.827-0.858)) levels decreased. For composite indicators, most of them existed a significant relationship to the overall cancer risk. Most indicators were correlated with the overall cancer and obesity-related cancer risk, but there was a reduction of association with non-obesity related cancer risk. Most of indicators mediated the association between anthropometric measurements and overall cancer risk. Conclusions: Systemic inflammatory state was significantly associated with increased risks of cancer risk. Inflammation biomarkers were found to partly mediate the association between obesity and cancer risk.

Predicting oral and esophageal cancers by one model in a Chinese prospective cohort study.

OBJECTIVE: Oral and esophageal cancers are both upper gastrointestinal cancers that share a number of risk factors. However, most previous risk prediction models only focused on one of these two types of cancer. There is no single model that could predict both cancers simultaneously. Our objective was to develop a model specifically tailored for oral and esophageal cancers. METHODS: From 1996 to 2007, a total of 431,460 subjects aged 20 and older without a history of cancer at baseline were included and were monitored for an average duration of 7.3 years in Taiwan, China. A total of 704 cases of oral and esophageal cancers were detected. We utilized both univariate and multivariate COX regression for screening predictors and constructing the model. We evaluated the goodness of fit of the model based on discriminatory accuracy, Harrell's C-index, and calibration. RESULTS: Finally, we developed a Cox regression model using the twelve most significant variables: age, gender, alcohol consumption, betel chewing, smoking status, history of oral ulceration, educational level, marital status, oropharynx status, family history of nasopharyngeal carcinoma, volume ratio of blood cell, and gamma-glutamyl transferase. The AUC (area under the curve) for the complete model was 0.82. Additionally, the C-index was 0.807 (with a 95 % confidence interval ranging from 0.789 to 0.824) and internal calibration results demonstrated that the model performed well. CONCLUSIONS: This study identified the twelve most significant common risk factors for oral and esophageal cancers and developed a single prediction model that performs well for both types of cancer.

Impaired lung function and lung cancer risk in 461 183 healthy individuals: a cohort study.

BACKGROUND: It has been known that smoking and various lung diseases including lung cancer can cause lung function impairment. However, the impact of different types of lung function impairments, such as preserved ratio impaired spirometry (PRISm) and airflow obstruction (AO), on the incidence and mortality of lung cancer in both general and never-smoker populations remains unclear. We wished to examine the effect of lung function impairments on lung cancer risks. METHODS: This was a retrospective cohort study (1 January 1994 to 31 December 2017) of individuals from a health surveillance programme in Taiwan who underwent baseline spirometry tests at the entry point. PRISm was defined as an FEV1/FVC (forced expiratory volume in 1 s/forced vital capacity) ratio >0.7 and FEV1 <0.8, while AO was defined as an FEV1/FVC ratio <0.7. Cox proportional hazards models and cubic spline curves were used to examine the associations between lung function impairments and lung cancer risks. RESULTS: The study included 461,183 individuals, of whom 14.3% had PRISm and 7.9% had AO. A total of 4038 cases of lung cancer and 3314 lung cancer-related deaths were identified during the 23 years of follow-up. Individuals with PRISm and AO exhibited a higher risk of lung cancer incidence and mortality compared with those with normal lung function. The adjusted HRs and 95% CIs were 1.14 (1.03 to 1.26) and 1.23 (1.10 to 1.37) in the overall cohort, and 1.08 (0.93 to 1.24), and 1.23 (1.05 to 1.45) in the never-smoker cohort. The risks of both developing and dying of lung cancer increased with the severity levels of lung function impairments and lower FEV1 values. CONCLUSION: Impaired lung function is associated with increased risks of developing lung cancer and subsequent mortality. The study highlights the importance of considering lung function in lung cancer screening for better candidate selection.

Occupational Sitting Time, Leisure Physical Activity, and All-Cause and Cardiovascular Disease Mortality.

Importance: For the first time, the 2020 World Health Organization guidelines on physical activity recommended reducing sedentary behaviors owing to their health consequences. Less is known on the specific association of prolonged occupational sitting with health, especially in the context of low physical activity engagement. Objective: To quantify health risks associated with prolonged occupational sitting and to determine whether there is a certain threshold of physical activity that may attenuate it. Design, Setting, and Participants: This prospective cohort study included participants in a health surveillance program in Taiwan who were followed-up between 1996 and 2017. Data on occupational sitting, leisure-time physical activity (LTPA) habits, lifestyle, and metabolic parameters were collected. Data analysis was performed in December 2020. Main Outcomes and Measures: The all-cause and cardiovascular disease (CVD) mortality associated with 3 occupational sitting volumes (mostly sitting, alternating sitting and nonsitting, and mostly nonsitting) were analyzed applying multivariable Cox regression models to calculate the hazard ratios (HRs) for all participants and by subgroups, including 5 LTPA levels and a personal activity intelligence (PAI)-oriented metric. Deaths occurring within the initial 2 years of follow-up were excluded to prevent reverse causality. Results: The total cohort included 481 688 participants (mean [SD] age, 39.3 [12.8] years; 256 077 women [53.2%]). The study recorded 26 257 deaths during a mean (SD) follow-up period of 12.85 (5.67) years. After adjusting for sex, age, education, smoking, drinking, and body mass index, individuals who mostly sat at work had a 16% higher all-cause mortality risk (HR, 1.16; 95% CI, 1.11-1.20) and a 34% increased mortality risk from CVD (HR, 1.34; 95% CI, 1.22-1.46) compared with those who were mostly nonsitting at work. Individuals alternating sitting and nonsitting at work did not experience increased risk of all-cause mortality compared with individuals mostly nonsitting at work (HR, 1.01; 95% CI, 0.97-1.05). For individuals mostly sitting at work and engaging in low (15-29 minutes per day) or no (<15 minutes per day) LTPA, an increase in LTPA by 15 and 30 minutes per day, respectively, was associated with a reduction in mortality to a level similar to that of inactive individuals who mostly do not sit at work. In addition, individuals with a PAI score exceeding 100 experienced a notable reduction in the elevated mortality risk associated with prolonged occupational sitting. Conclusions and Relevance: As part of modern lifestyles, prolonged occupational sitting is considered normal and has not received due attention, even though its deleterious effect on health outcomes has been demonstrated. In this study, alternating between sitting and nonsitting at work, as well as an extra 15 to 30 minutes per day of LTPA or achieving a PAI score greater than 100, attenuated the harms of prolonged occupational sitting. Emphasizing the associated harms and suggesting workplace system changes may help society to denormalize this common behavior, similar to the process of denormalizing smoking.

Uncovering a dose-response relationship between positive fecal immunochemical test (FIT) and all-cause, cardiovascular and cancer-related mortality.

BACKGROUND: Fecal immunochemical test (FIT) is for colorectal cancer (CRC) screening. Its association with non-CRC mortality has been overlooked. Given the quantitative FIT values, its dose-response relationships with different causes of deaths and years of life shortened were assessed. METHODS: This retrospective study included 546,214 adults aged ≥ 20 who attended a health surveillance program from 1994 to 2017 and were followed up until the end of 2020. FIT ≥ 20 µg Hb/g was defined as positive. The Cox model was used to assess adjusted hazard ratios (aHR). RESULTS: Positive FIT was associated with increased all-cause mortality (aHR: 1.34, 95 % CI: 1.25-1.44) and all-cancer mortality (aHR: 1.71, 95 % CI: 1.55-1.89), with a reduction of life expectancy by 4 years. The association remained even with CRC excluded. With each 10 µg Hb/g increase in FIT above 20 µg Hb/g, life expectancy was reduced by one year, and mortality increased by 4 %. About 18.6 % of deaths with positive FIT were attributed to cardiovascular disease (CVD), followed by CRC (13.5 %) and upper gastrointestinal (GI) cancers (4.5 %). The all-cause mortality rate after excluding CRC for positive FIT was 3.56/1,000 person-year, comparable to the all-cause mortality rate of 3.69/1,000 person-year for hypertension. CONCLUSION: Positive FIT was associated with increased mortality in a dose-response manner and shortened life expectancy by 4 years, an overlooked risk comparable to hypertension, even with CRC excluded. After a negative colonoscopy, subjects with positive FIT should undergo a workup on CVD risk factors and look for other upper GI cancers.

Resting Heart Rate Independent of Cardiovascular Disease Risk Factors Is Associated With End-Stage Renal Disease: A Cohort Study Based on 476 347 Adults.

BACKGROUND: The relationship between resting heart rate (RHR) and the risk of end-stage renal disease (ESRD) among those without cardiovascular disease remains unclear. We aim to establish temporal consistency and elucidate the independent relationship between RHR and the risk of ESRD. METHODS AND RESULTS: This cohort enrolled participants from 476 347 individuals who had taken part in a screening program from 1996 to 2017. We identified 2504 participants who had ESRD, and the median follow-up was 13 years. RHR was extracted from electrocardiography results, and the study assessed the relationship between RHR and the risk of ESRD using the Cox proportional hazards model. Of the participants, 32.6% had an RHR of 60 to 69 beats per minute (bpm), and 22.2% had an RHR of ≥80 bpm. Participants with an RHR of ≥80 bpm had a higher stage of chronic kidney disease, lower estimated glomerular filtration rate, and more proteinuria than those with an RHR of 60 to 69 bpm. Participants with an RHR of 80 to 89 and ≥90 bpm had a 24% (hazard ratio [HR], 1.24 [95% CI, 1.09-1.42]) and 64% (HR, 1.64 [95% CI, 1.42-1.90]) higher risk of ESRD, respectively. The risk of ESRD remained significantly elevated (HR, 1.32 [95% CI, 1.10-1.58] per 10-beat increase from 60 bpm) after excluding participants who smoked; had hypertension, diabetes, or hyperlipidemia; or were overweight. CONCLUSIONS: An RHR of ≥80 bpm is significantly associated with an increased risk of ESRD. These results suggest that RHR may serve as a risk factor for kidney disease in individuals without established cardiovascular disease risk factors.

Association of fat mass and fat-free mass with all-cause and cause-specific mortality in Asian individuals: A prospective cohort study.

OBJECTIVE: The study's objective was to investigate the association of fat mass index (FMI) and fat-free mass index (FFMI) with all-cause mortality and cause-specific mortality in the Chinese population. METHODS: A total of 422,430 participants (48.1% men and 51.9% women) from the Taiwan MJ Cohort with an average follow-up of 9 years were included. RESULTS: The lowest (Q1) and highest (Q5) quintiles of FMI and FFMI were associated with increased all-cause mortality. Compared with those in the third quintile (Q3) group of FMI, participants in Q1 and Q5 groups of FMI had hazard ratios and 95% CI of 1.32 (1.24-1.40) and 1.13 (1.06-1.20), respectively. Similarly, compared with those in Q3 group of FFMI, people in Q1 and Q5 groups of FFMI had hazard ratios of 1.14 (1.06-1.23) and 1.16 (1.10-1.23), respectively. In the restricted cubic spline models, both FMI and FFMI showed a J-shaped association with all-cause mortality. People in Q5 group of FFMI had a hazard ratio of 0.72 (0.58-0.89) for respiratory disease. CONCLUSIONS: The mortality risk increases in those with excessively high or low FMI and FFMI, yet the associations between FMI, FFMI, and the risk of death varied across subgroups and causes of death.

Association between plant-based dietary pattern and biological aging trajectory in a large prospective cohort.

BACKGROUND: Aging is a dynamic and heterogeneous process that may better be captured by trajectories of aging biomarkers. Biological age has been advocated as a better biomarker of aging than chronological age, and plant-based dietary patterns have been found to be linked to aging. However, the associations of biological age trajectories with mortality and plant-based dietary patterns remained unclear. METHODS: Using group-based trajectory modeling approach, we identified distinctive aging trajectory groups among 12,784 participants based on a recently developed biological aging measure acquired at four-time points within an 8-year period. We then examined associations between aging trajectories and quintiles of plant-based dietary patterns assessed by overall plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI) among 10,191 participants who had complete data on dietary intake, using multivariable multinomial logistics regression adjusting for sociodemographic and lifestyles factors. Cox proportional hazards regression models were applied to investigate the association between aging trajectories and all-cause mortality. RESULTS: We identified three latent classes of accelerated aging trajectories: slow aging, medium-degree, and high-degree accelerated aging trajectories. Participants who had higher PDI or hPDI had lower odds of being in medium-degree (OR = 0.75, 95% CI: 0.65, 0.86 for PDI; OR = 0.73, 95% CI: 0.62, 0.85 for hPDI) or high-degree (OR = 0.63, 95% CI: 0.46, 0.86 for PDI; OR = 0.62, 95% CI: 0.44, 0.88 for hPDI) accelerated aging trajectories. Participants in the highest quintile of uPDI were more likely to be in medium-degree (OR = 1.72, 95% CI: 1.48, 1.99) or high-degree (OR = 1.70, 95% CI: 1.21, 2.38) accelerated aging trajectories. With a mean follow-up time of 8.40 years and 803 (6.28%) participants died by the end of follow-up, we found that participants in medium-degree (HR = 1.56, 95% CI: 1.29, 1.89) or high-degree (HR = 3.72, 95% CI: 2.73, 5.08) accelerated aging trajectory groups had higher risks of death than those in the slow aging trajectory. CONCLUSIONS: We identified three distinctive aging trajectories in a large Asian cohort and found that adopting a plant-based dietary pattern, especially when rich in healthful plant foods, was associated with substantially lowered pace of aging.

6-Year trajectory of fasting plasma glucose (FPG) and mortality risk among individuals with normal FPG at baseline: a prospective cohort study.

BACKGROUND: Higher fasting plasma glucose (FPG) levels were associated with an increased risk of all-cause mortality; however, the associations between long-term FPG trajectory groups and mortality were unclear, especially among individuals with a normal FPG level at the beginning. The aims of this study were to examine the associations of FPG trajectories with the risk of mortality and identify modifiable lifestyle factors related to these trajectories. METHODS: We enrolled 50,919 individuals aged ≥ 20 years old, who were free of diabetes at baseline, in the prospective MJ cohort. All participants completed at least four FPG measurements within 6 years after enrollment and were followed until December 2011. FPG trajectories were identified by group-based trajectory modeling. We used Cox proportional hazards models to examine the associations of FPG trajectories with mortality, adjusting for age, sex, marital status, education level, occupation, smoking, drinking, physical activity, body mass index, baseline FPG, hypertension, dyslipidemia, cardiovascular disease or stroke, and cancer. Associations between baseline lifestyle factors and FPG trajectories were evaluated using multinomial logistic regression. RESULTS: We identified three FPG trajectories as stable (n = 32,481), low-increasing (n = 17,164), and high-increasing (n = 1274). Compared to the stable group, both the low-increasing and high-increasing groups had higher risks of all-cause mortality (hazard ratio (HR) = 1.18 (95% CI 0.99-1.40) and 1.52 (95% CI 1.09-2.13), respectively), especially among those with hypertension. Compared to participants with 0 to 1 healthy lifestyle factor, those with 6 healthy lifestyle factors were more likely to be in the stable group (ORlow-increasing = 0.61, 95% CI 0.51-0.73; ORhigh-increasing = 0.20, 95% CI 0.13-0.32). CONCLUSIONS: Individuals with longitudinally increasing FPG had a higher risk of mortality even if they had a normal FPG at baseline. Adopting healthy lifestyles may prevent individuals from transitioning into increasing trajectories.

Association of sleep duration and sleeping pill use with mortality and life expectancy: A cohort study of 484,916 adults.

OBJECTIVES: To compare mortality risk and life expectancy among individuals with different sleep durations and sleeping pill use. METHODS: A cohort of 484,916 community-dwelling adults in Taiwan was recruited into a health screening program from 1994 to 2011. Subjects were categorized by daily sleep duration into 4 groups: extremely short (<4 hours), short (4-6 hours), medium (6-8 hours), and long (>8 hours). Cox proportional hazards models were used to investigate the associations of mortality risk with sleep duration and sleeping pill use. Models were adjusted for sociodemographic characteristics, lifestyle, and comorbidities. Life expectancy tables were calculated among sleeping pill users and nonusers with different sleep durations. RESULTS: With 6- 8 hours of daily sleep, sleeping pill nonusers had the lowest mortality risk. Sleeping pill users, even with this optimal amount of sleep, had a 55% (p < .001, 95% CI, 1.38-1.73) higher mortality risk than nonusers. The life expectancy of 30-year-old male sleeping pill users with extremely short or long sleep durations was 12-13 years shorter than sleeping pill nonusers who had 6-8 hours of sleep. On average, life expectancy in individuals using sleeping pills (vs. nonusers) was shorter by 5.3 (95% CI, 4.10-6.32) years in men and 5.7 (95% CI, 5.28-7.98) years in women. CONCLUSIONS: This study suggests that the use of sleeping pills is associated with an increased risk of mortality and shortened life expectancy, especially in extreme sleepers. Regular users should be aware of potential harms from sleeping pills.

The Kidney Failure Risk Equation: Evaluation of Novel Input Variables including eGFR Estimated Using the CKD-EPI 2021 Equation in 59 Cohorts.

SIGNIFICANCE STATEMENT: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict 2- and 5-year risk of kidney failure in populations with eGFR <60 ml/min per 1.73 m 2 . However, the CKD-EPI 2021 creatinine equation for eGFR is now recommended for use but has not been fully tested in the context of KFRE. In 59 cohorts comprising 312,424 patients with CKD, the authors assessed the predictive performance and calibration associated with the use of the CKD-EPI 2021 equation and whether additional variables and accounting for the competing risk of death improves the KFRE's performance. The KFRE generally performed well using the CKD-EPI 2021 eGFR in populations with eGFR <45 ml/min per 1.73 m 2 and was not improved by adding the 2-year prior eGFR slope and cardiovascular comorbidities. BACKGROUND: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict kidney failure risk in people with GFR <60 ml/min per 1.73 m 2 . METHODS: Using 59 cohorts with 312,424 patients with CKD, we tested several modifications to the KFRE for their potential to improve the KFRE: using the CKD-EPI 2021 creatinine equation for eGFR, substituting 1-year average ACR for single-measure ACR and 1-year average eGFR in participants with high eGFR variability, and adding 2-year prior eGFR slope and cardiovascular comorbidities. We also assessed calibration of the KFRE in subgroups of eGFR and age before and after accounting for the competing risk of death. RESULTS: The KFRE remained accurate and well calibrated overall using the CKD-EPI 2021 eGFR equation. The other modifications did not improve KFRE performance. In subgroups of eGFR 45-59 ml/min per 1.73 m 2 and in older adults using the 5-year time horizon, the KFRE demonstrated systematic underprediction and overprediction, respectively. We developed and tested a new model with a spline term in eGFR and incorporating the competing risk of mortality, resulting in more accurate calibration in those specific subgroups but not overall. CONCLUSIONS: The original KFRE is generally accurate for eGFR <45 ml/min per 1.73 m 2 when using the CKD-EPI 2021 equation. Incorporating competing risk methodology and splines for eGFR may improve calibration in low-risk settings with longer time horizons. Including historical averages, eGFR slopes, or a competing risk design did not meaningfully alter KFRE performance in most circumstances.

Risk of Death Associated With Reversion From Prediabetes to Normoglycemia and the Role of Modifiable Risk Factors.

Importance: Individuals with prediabetes have a higher risk of death than healthy individuals. However, previous findings have suggested that individuals with reversion from prediabetes to normoglycemia may not have a lower risk of death compared with individuals with persistent prediabetes. Objectives: To investigate the associations between changes in prediabetes status and risk of death and to elucidate the roles of modifiable risk factors in these associations. Design, Setting, and Participants: This population-based prospective cohort study used data from 45 782 participants with prediabetes from the Taiwan MJ Cohort Study who were recruited between January 1, 1996, and December 31, 2007. Participants were followed up from the second clinical visit to December 31, 2011, with a median (IQR) follow-up of 8 (5-12) years. Participants were categorized into 3 groups according to changes in their prediabetes status within a 3-year period after initial enrollment: reversion to normoglycemia, persistent prediabetes, and progression to diabetes. Cox proportional hazards regression models were used to examine the associations between changes in prediabetes status at baseline (ie, the second clinical visit) and risk of death. Data analysis was performed between September 18, 2021, and October 24, 2022. Main Outcomes and Measures: All-cause mortality, cardiovascular disease (CVD)-related mortality, and cancer-related mortality. Results: Of 45 782 participants with prediabetes (62.9% male; 100% Asian; mean [SD] age, 44.6 [12.8] years), 1786 (3.9%) developed diabetes and 17 021 (37.2%) reverted to normoglycemia. Progression from prediabetes to diabetes within a 3-year period was associated with higher risks of all-cause death (hazard ratio [HR], 1.50; 95% CI, 1.25-1.79) and CVD-related death (HR, 1.61; 95% CI, 1.12-2.33) compared with persistent prediabetes, while reversion to normoglycemia was not associated with a lower risk of all-cause death (HR, 0.99; 95% CI, 0.88-1.10), cancer-related death (HR, 0.91; 95% CI, 0.77-1.08), or CVD-related death (HR, 0.97; 95% CI, 0.75-1.25). Among individuals who were physically active, reversion to normoglycemia was associated with a lower risk of all-cause death (HR, 0.72; 95% CI, 0.59-0.87) compared with those with persistent prediabetes who were physically inactive. Among individuals with obesity, risk of death varied between those who experienced reversion to normoglycemia (HR, 1.10; 95% CI, 0.82-1.49) and those who had persistent prediabetes (HR, 1.33; 95% CI, 1.10-1.62). Conclusions and Relevance: In this cohort study, although reversion from prediabetes to normoglycemia within a 3-year period did not mitigate the overall risk of death compared with persistent prediabetes, risk of death associated with reversion to normoglycemia varied based on whether individuals were physically active or had obesity. These findings highlight the importance of lifestyle modification among those with prediabetes status.

Development of a Novel Multidimensional Measure of Aging to Predict Mortality and Morbidity in the Prospective MJ Cohort.

BACKGROUND: Although biological aging has been proposed as a more accurate measure of aging, few biological aging measures have been developed for Asians, especially for young adults. METHODS: A total of 521 656 participants were enrolled in the MJ cohort (1996-2011) and were followed until death, loss-to-follow-up, or December 31, 2011, whichever came first. We selected 14 clinical biomarkers, including chronological age, using a random forest algorithm, and developed a multidimensional aging measure (MDAge). Model performance was assessed by area under the curve (AUC) and internal calibration. We evaluated the associations of MDAge and residuals from regressing MDAge on chronological age (MDAgeAccel) with mortality and morbidity, and assessed the robustness of our findings. RESULTS: MDAge achieved an excellent AUC of 0.892 in predicting all-cause mortality (95% confidence interval [CI]: 0.889-0.894). Participants with higher MDAge at baseline were at a higher risk of death (per 5 years, hazard ration [HR] = 1.671, 95% CI: 1.662-1.680), and the association remained after controlling for other variables and in different subgroups. Furthermore, participants with higher MDAgeAccel were associated with shortened life expectancy. For instance, compared to men who were biologically younger (MDAgeAccel ≤ 0) at baseline, men in the highest tertiles of MDAgeAccel had shortened life expectancy by 17.23 years. In addition, higher MDAgeAccel was associated with having chronic disease either cross-sectionally (per 1-standard deviation [SD], odds ratio [OR] = 1.564, 95% CI: 1.552-1.575) or longitudinally (per 1-SD, OR = 1.218, 95% CI: 1.199-1.238). CONCLUSION: MDAge accurately predicted mortality and morbidity, which has great potential in the early identification of individuals at higher risk and therefore promoting early intervention.

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Associations of Prolonged Occupational Sitting with the Spectrum of Kidney Disease: Results from a Cohort of a Half-Million Asian Adults.

BACKGROUND: Kidney diseases are viewed as continuously progressing diseases from microalbuminuria and chronic kidney disease (CKD), to end-stage renal disease (ESRD) and its mortality including deaths. The report on the association between prolonged sitting and kidney diseases is limited. METHODS: We examined a cohort of 455,506 participants in a screening program in Taiwan conducted between 1996 and 2017. Data on occupational sedentary behavior and physical activity were collected with a standardized questionnaire. The outcomes of ESRD and death were identified by linking with the Catastrophic Illness Dataset and Cause of Death Data. The association between prolonged sitting and CKD, the incidence of ESRD, and death were assessed using logistic regression models to compute odds ratios (ORs) and Cox proportional hazards models for hazard ratios (HRs). RESULTS: More than half of the participants, i.e., 265,948 (58.4%), were categorized as "prolonged sitting" during their work. During a median of 13 years of follow-up, we identified 2227 individuals undergoing dialysis and 25,671 deaths. Prolonged occupational sitting was significantly associated with a higher risk of CKD (OR: 1.26, 95% confidence interval: 1.21, 1.31), ESRD (HR: 1.19, 95% CI 1.03, 1.38), and kidney-specific mortality (HR: 1.43, 95% CI 1.07, 1.91) compared to mostly standing participants after controlling for physical activity and other risk factors. Inactive prolonged sitting carries a significantly higher risk of ESRD than physically active mostly standing participants (HR: 1.34, 95% CI 1.04, 1.73). However, active prolonged sitting decreased the risk of ESRD (HR: 1.03, 95% CI 0.79, 1.34) compared to inactive prolonged sitting. CONCLUSION: The results suggest that prolonged occupational sitting is associated with a greater risk of the spectrum of kidney disease, proteinuria, CKD, dialysis (ESRD), and mortality for all causes and kidney diseases. Physical activity, even at a minimal level of 15 min/day (90 min/week) of moderate-intensity exercise, was associated with a reduction in these risks.

Personal Activity Intelligence and Ischemic Heart Disease in a Healthy Population: China Kadoorie Biobank Study.

BACKGROUND: Personal Activity Intelligence (PAI) is a physical activity metric that translates heart rate during physical activity into a simple score, where a weekly score of 100 or greater is associated with a lower risk of cardiovascular disease and mortality. Here, we prospectively investigated the association between PAI and ischemic heart disease (IHD) mortality in a large healthy population from China. METHODS: Using data from the China Kadoorie Biobank, we studied 443,792 healthy adults (60% women). The weekly PAI score of each participant was estimated based on the questionnaire data and divided into four groups (PAI scores of 0, ≤50, 51-99, or ≥100). Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for fatal IHD and nonfatal myocardial infraction (MI) related to PAI were estimated using Cox proportional hazard regression analyses. RESULTS: There were 3050 IHD deaths and 1808 MI events during a median follow-up of 8.2 years (interquartile range, 7.3-9.1; 3.6 million person-years). After adjustments for multiple confounders, a weekly PAI score ≥ 100 was associated with a lower risk of IHD (aHR: 0.91 (95% CI: 0.83-1.00)), compared with the inactive group (0 PAI). The corresponding aHR for MI was 0.94 (95% CI: 0.83-1.05). In participants aged 60 years or older at baseline, the aHR associated with a weekly PAI score ≥ 100 was 0.84 (95% CI, 0.75-0.93) for IHD and 0.84 (95% CI, 0.73-0.98) for MI. CONCLUSION: Among healthy Chinese adults, a weekly PAI score of 100 or greater was associated with a lower risk of IHD mortality across all age groups; moreover, a high PAI score significantly lowered the risk of MI but only in those 60 years and older at baseline. The present findings extend the scientific evidence that PAI may have prognostic significance in diverse settings for IHD outcomes and suggest that the PAI metric may be useful in delineating the magnitude of weekly physical activity needed to reduce the risk of IHD mortality.

A prediction model with lifestyle factors improves the predictive ability for renal replacement therapy: a cohort of 442 714 Asian adults.

Background: There are limited renal replacement therapy (RRT) prediction models with good performance in the general population. We developed a model that includes lifestyle factors to improve predictive ability for RRT in the population at large. Methods: We used data collected between 1996 and 2017 from a medical screening in a cohort comprising 442 714 participants aged 20 years or over. After a median follow-up of 13 years, we identified 2212 individuals with end-stage renal disease (RRT, n: 2091; kidney transplantation, n: 121). We built three models for comparison: model 1: basic model, Kidney Failure Risk Equation with four variables (age, sex, estimated glomerular filtration rate and proteinuria); model 2: basic model + medical history + lifestyle risk factors; and model 3: model 2 + all significant clinical variables. We used the Cox proportional hazards model to construct a points-based model and applied the C statistic. Results: Adding lifestyle factors to the basic model, the C statistic improved in model 2 from 0.91 to 0.94 (95% confidence interval: 0.94, 0.95). Model 3 showed even better C statistic value i.e., 0.95 (0.95, 0.96). With a cut-off score of 33, model 3 identified 3% of individuals with RRT risk in 10 years. This model detected over half of individuals progressing to RRT, which was higher than the sensitivity of cohort participants with stage 3 or higher chronic kidney disease (0.53 versus 0.48). Conclusions: Our prediction model including medical history and lifestyle factors improved the predictive ability for end-stage renal disease in the general population in addition to chronic kidney disease population.