Routine Health Check-up Research Articles

Validity of the Chinese multimorbidity-weighted index in measuring disease burden using health check-ups data in primary care

BackgroundAs multimorbidity becomes common that imposes a considerable burden to patients, but the extent to which widely-used multimorbidity indexes can be applied to quantify disease burden using primary care data in China is not clear. We applied the Chinese Multimorbidity-Weighted Index (CMWI) to health check-ups data routinely collected among older adults by primary care, to examine its validity in measuring multimorbidity associated risks of disability and mortality in annual follow-ups.MethodsThe study utilized data from annual health check-ups of older adults, which included information on individual age, sex, and 14 health conditions at primary care in a district of Guangzhou, Guangdong, China. The risk of CMWI for mortality was analysed in a total sample of 45,009 persons 65 years and older between 2014 and 2020 (average 2.70-year follow-up), and the risk for disability was in a subsample of 18,320 older adults free of physical impairment in 2019 and followed-up in 2020. Risk of death and disability were assessed with Cox proportional hazard regression and binary logistic regression, respectively, with both models adjusted for age and sex variables. The model fit was assessed by the Akaike information criterion (AIC), and C-statistic or the area under the receiver operating characteristic curve (AUC).ResultsOne unit increase in baseline-CMWI (Median= 1.70, IQR: 1.30-3.00) was associated with higher risk in subsequent disability (OR = 1.12, 95%CI = 1.05,1.20) and mortality (OR = 1.18, 95%CI = 1.14, 1.22). Participants in the top tertile of CMWI had 99% and 152% increased risks of disability and mortality than their counterparts in the bottom tertile. Model fit was satisfied with adequate AUC (0.84) or C-statistic (0.76) for both outcomes.ConclusionsCMWI, calculated based on primary care’s routine health check-ups data, provides valid estimates of disability and mortality risks in older adults. This validated tool can be used to quantity and monitor older patients’ health risks in primary care.

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers. Based on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables. A total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors. The ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases.

Novel machine learning algorithm in risk prediction model for pan-cancer risk: application in a large prospective cohort

ObjectiveTo develop and validate machine-learning models that predict the risk of pan-cancer incidence using demographic, questionnaire and routine health check-up data in a large Asian population.Methods and analysisThis study is...

Association between estimated glucose disposal rate and subclinical coronary atherosclerosis

Background and aimsThe estimated glucose disposal rate (eGDR) is an easily accessible clinical parameter for assessing insulin resistance in patients with diabetes mellitus. In this study, we aimed to investigate the link between eGDR and subclinical coronary atherosclerosis in an asymptomatic middle-aged Korean population. Methods and resultsThis study involved 4004 subjects who underwent routine health checkups with coronary multidetector computed tomography (MDCT) at Asan Medical Center from 2007 to 2011, among whom 913 had a follow-up analysis through 2014. The eGDR was calculated using: 21.16 − (0.09 ∗ waist circumference [cm]) − (3.41 ∗ hypertension) − (0.55 ∗ glycated hemoglobin [%]). Patients were categorized into three groups according to the tertiles of eGDR. Subclinical coronary atherosclerosis was defined by significant coronary stenosis (≥50%), presence of plaques, coronary artery calcification (CAC) score, and its progression. As a result, a lower eGDR level was associated with higher prevalence of significant coronary stenosis, plaques, moderate to severe CAC, and CAC progression. Compared to other markers or risk scores, eGDR was superior to other biomarkers of insulin resistance but did not provide additional information beyond classic cardiovascular risk models like the Framingham Risk Score and Pooled Cohort Equations. ConclusionDecreased eGDR values were significantly associated with higher subclinical coronary atherosclerosis burdens in an asymptomatic middle-aged Korean population. However, its clinical implications remain uncertain due to its weaker performance compared to established cardiovascular risk models.

Increasing public awareness through routine health checks for residents of RT 21 Kelurahan 13 Ulu, Palembang

Health check-ups are one of the efforts to detect abnormalities in the body that should be performed routinely with the aim of prevention and early treatment. The purpose of this activity is to increase public knowledge about the importance of health check-ups. With increased public awareness of the importance of health check-ups, their desire to undergo health checks also increases, allowing for earlier disease identification. The method used involves counseling with leaflet media and community health check-ups. Based on health screenings conducted in Kelurahan 13 Ulu RT 021 RW 005, Seberang Ulu II District, Palembang City, the results indicated an increase in knowledge, understanding, and public awareness regarding regular health checks. This is expected to encourage a change in community behavior towards routine health check-ups, leading to earlier disease detection and prevention of more serious illnesses.

Fuzzy rule based classifier model for evidence based clinical decision support systems

Clinicians benefit from the use of artificial intelligence and machine learning techniques applied to health data within health records, which identify commonalities between them. It enables them to get evidence-based support in recommending shared treatment paths for undiagnosed health records. The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential features, supporting public health experts in their management of population health conditions. This paper presents a novel mapping tool model designed to analyze electronic health records and provide healthcare providers with evidence-based decision support. The work focuses on the analysis of health records from hospital databases, encompassing parameters extracted from routine health checkups. By scrutinizing patterns within examined health records, healthcare providers can seamlessly align with newer health records for diagnosis and treatment recommendations. Core to this approach is the integration of a fuzzy rule-based classifier system within the proposed system. This incorporation facilitates the processing of health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a specially developed distance-measure technique tailored for the proposed fuzzy-based system. Results showcase satisfying performance and robust discriminant capability for accurate recommendations. The alignment of outcomes with expert evaluations underscores the model's efficacy and attainment of benchmarks.

Biological variation of 16 biochemical analytes estimated from a large clinicopathologic database of dogs and cats.

Biochemical measurements are commonly evaluated using population-based reference intervals; however, there is a growing trend toward reassessing results with within-subject variation (CVI). We aimed to estimate the CVI of 16 biochemical analytes using a large database of dogs and cats, which refers to the results of routine health checkups. Pairs of sequential results for 16 analytes were extracted from a database of adult patients. The second result was divided by the first result to produce the ratio of sequential results (rr), and the frequency distribution of rr was plotted. From the plots, the coefficient of variation (CVrr) was calculated. Analytical variation (CVA) was calculated using quality control data, and CVI was estimated as follows: . Estimated CVI was compared with previously reported CVI using the Bland-Altman plot analysis. From the database, 9078 data points from 3610 dogs and 3743 data points from 1473 cats were extracted, with 5468 data pairs for dogs and 2270 for cats. Sampling intervals ranged from 10 to 1970 days (median 366) for dogs and 23 to 1862 days (median 365) for cats. Bland-Altman analysis showed most CVI plots fell within the limits of agreement; however, positive fixed biases were observed in both dogs and cats. Our study introduces a novel approach of estimating CVI using routine health checkup data in dogs and cats. Despite biases, our method holds promise for clinical application in assessing the significance of measurement result differences.

Prevalence and Associated Factors of Depression Symptoms Among Aquaculture Workers in Hai Phong, Vietnam: A Cross-Sectional Study

Objective: This study aimed to determine the prevalence and factors associated with depression symptoms among aquaculture workers in Hai Phong, Vietnam. Material and Methods: The study involved 1,220 aquaculture workers in coastal and offshore areas of Hai Phong, Vietnam; from March 2022 to September 2022. It was a cross-sectional descriptive epidemiological study, utilizing direct interviews with aquaculture workers to identify depression symptoms and associated factors. The Depression-Anxiety- Stress Scale (DASS 21) was used to assess depression symptoms. Results: The prevalence of depression symptoms among aquaculture workers was 28.7%, with 20.9% experiencing mild depression, 6.3% moderate depression and 1.5% severe depression. Several factors were associated with depression symptoms; including gender (OR=2.03; 95% CI 1.56-2.63), being single or divorced (OR=1.44; 95% CI 1.04-1.99), age ≥60 (OR=1.63; 95% CI 1.05-2.55) compared to those under 40, chronic illnesses (OR=3.16; 95% CI 2.43-4.11), poor sleep quality (OR=2.84; 95% CI 2.13-3.79), alcohol abuse (OR=1.36; 95% CI 1.06-1.75), and irregular physical exercise (OR=2.09; 95% CI 1.62-2.70). Conclusion: Depression disorder is a mental health concern among aquaculture workers. To alleviate depression symptoms, workers should consider lifestyle adjustments, incorporate regular physical exercise, and undergo routine health check-ups for timely counseling and treatment.

#842 Validation of the excessive salt intake screening questionnaire in CKD patients

Abstract Background and Aims Excessive salt intake is implicated in the progression of hypertension, chronic kidney disease (CKD), diabetic nephropathy, cardiovascular diseases, cerebrovascular diseases, and increased mortality. In Japan, average salt consumption surpasses the levels recommended by national guidelines and the WHO. However, individuals seldom have the chance to gauge their salt intake accurately. This study aimed to validate a screening tool initially developed by Sasaki et al. to identify individuals with excessive salt intake using health check-up data from Asahi City, Chiba Prefecture, Japan. Method This study was conducted as a part of the Cities Changing Diabetes (CCD). CCD is Novo Nordisk's commitment to drive action against type 2 diabetes and obesity in cities globally. It is a partnership program to accelerate the prevention of diabetes and obesity by addressing the systemic change in urban environments. Participants of this study were drawn from specific health examinations in Asahi City. Consent was obtained, and dialysis patients were excluded. Spot urine samples were analyzed for sodium and creatinine to estimate daily salt intake via Tanaka's formula. The screening tool incorporated risk factors such as age, gender, Body Mass Index (obesity), hypertension (use of antihypertensive drugs), frequency of soup intake, saltiness of home-cooked meals compared to restaurants, efforts to reduce salt intake, and frequency of dining out. The primary outcome was excessive salt intake, defined as an intake exceeding one standard deviation above the mean. Discrimination was assessed using the Area Under the Curve (AUC), while calibration was evaluated with a calibration plot and the Hosmer-Lemeshow test. Results Of the 1532 CKD participants, the mean age was 76.3 years, with males comprising 46%. The average daily salt intake was 9.3 g (SD: 2.39), and the estimated Glomerular Filtration Rate (GFR) was 50.6 ml/min/1.73 m² (SD: 14.5). The distribution of CKD stages included 1198 stage 3a, 285 stage 3b, 43 stage 4, and 5 stage 5 patients. The AUC of screening tool was 0.65 (95% CI: 0.61 to 0.69), and its calibration slope (with an intercept) was 0.98 (95% CI: 0.72 to 1.25). The Hosmer-Lemeshow test yielded a p-value of 0.626. Conclusion The predictive capability of the existing model for excessive salt intake was limited in the CKD population, which may suggest a need for a screening method distinct from that used in the general population. Factors such as changes in sodium excretion due to reduced renal function and the effects of diuretics should also be considered when interpreting these results. Integrating this questionnaire into routine health check-ups could enhance dietary monitoring and intervention, potentially attenuating the progression of CKD and its associated complications. There remains scope for further refinement of the tool's accuracy by incorporating additional predictors.

Elevated LDL-c may warn of the risk of gallbladder stones in the patients with metabolic dysfunction-associated steatotic liver disease: A case-control study

PurposeThis study aimed to explore the relationship between metabolic dysfunction-associated steatotic liver disease (MASLD) and the development of new-onset gallbladder stone disease (GSD) and to identify factors that influence the occurrence of new-onset GSD in patients with MASLD. MethodsIn this retrospective case-control study, patients who underwent asymptomatic GSD screening during annual routine health check-ups at two hospitals in China between August 2017 and July 2022 were included. Patients with new-onset GSD and controls without GSD were matched 1:1 based on age, sex, race, occupation, diet, drinking habits, systolic blood pressure, diastolic blood pressure, and fasting blood glucose levels. ResultsThe study comprised 1200 patients with new-onset GSD and 1200 controls without GSD. Patients with new-onset GSD had higher rates of MASLD (33.8% vs. 22.2 %, P < 0.001) and hypercholesterolemia (12.6% vs. 7.2 %, P < 0.001) compared to controls. Waist circumference (WC) (OR = 1.042, 95 % CI: 1.022–1.063, P < 0.001), high-density lipoprotein cholesterol (HDL-c) (OR = 0.048, 95 % CI: 0.037–0.062, P < 0.001), triglycerides (OR = 0.819, 95 % CI: 0.699–0.958, P = 0.013), and hypercholesterolemia (OR = 5.023, 95 % CI: 2.735–9.225, P < 0.001) were independently associated with new-onset GSD. Among patients with MASLD, WC (OR = 1.075, 95 % CI: 1.026–1.127, P = 0.003), total cholesterol (TC) (OR = 2.094, 95 % CI: 1.259–3.484, P = 0.004), HDL-c (OR = 0.088, 95 % CI: 0.054–0.142, P < 0.001), and low-density lipoprotein cholesterol (LDL-c) (OR = 4.056, 95 % CI: 2.669–6.163, P < 0.001) were independently associated with new-onset GSD. ConclusionsThe findings indicate that hypercholesterolemia is independently associated with GSD. Among patients with MASLD, hypercholesterolemia also showed an independent association with GSD. Notably, this study is the first to identify serum LDL-c levels as potentially the most significant risk factor for GSD, highlighting that elevated LDL-c could serve as an important indicator for individuals with MASLD.

Awareness of HPV vaccine and its socio-demographic determinants among the parents of eligible daughters in Bangladesh: A nationwide study

BackgroundVaccination against Human papillomavirus (HPV) is recommended to avoid HPV infections and its associated diseases, including cervical cancer. However, there is no awareness study among Bangladeshi population. Hence, this nationwide study was conducted to explore HPV vaccine awareness and its determinants among parents of eligible adolescent girls. MethodsThis study was conducted among the parents of daughters aged 9–15 years from 42 out of 64 randomly selected districts of Bangladesh between June 28 to August 2, 2023. A multistage sampling method was used to enroll 2151 study participants from all eight divisions of Bangladesh. A semi-structured questionnaire was used for face-to-face interviews in this study. The statistical software Stata (Version 17) was used for statistical analyses. ResultsThe average age of the participants was 38.18 (±5.86) years. Only 22.32 % of the participants were aware of the HPV vaccine. Every additional year of age increased the likelihood of being aware of the HPV vaccine by 3 % (AOR: 1.03; 95%CI: 1.00–1.06). Participants residing in the urban area had 3.56 times higher odds of awareness than rural and semi-urban people. Businessmen and housewives had 60 % (AOR: 0.40; 95 % CI: 0.22–0.69) and 77 % (AOR: 0.23; 95 % CI: 0.16–0.33) lower odds in comparison to job holders. The lower-income group exhibited significantly higher odds of awareness (AOR: 0.25, 95%CI: 0.16–0.39) compared to the middle and the higher-income group. Participants who never went through routine health check-ups had 77 % lower odds of being aware than those who availed of regular routine check-ups (AOR: 0.23; 95%CI: 0.16–0.34). ConclusionAwareness of the HPV vaccine among the general population of Bangladesh is very low. Age, residence, occupation, monthly income, and routine medical check-ups were associated with HPV vaccine awareness. A nationwide awareness campaign would increase this awareness level among the Bangladeshi population, especially among the parents of daughters.

Longitudinal changes in optic disc cupping from the baseline in chiasmal lesion optic neuropathy and glaucoma

We aimed to investigate the changes in cupping in chiasmal lesion optic neuropathy (chON) compared to baseline optic disc and glaucoma. We used a novel study design to enroll patients who had fundus photographs incidentally taken during routine health check-ups prior to the onset of optic neuropathy. In 31 eyes (21 patients) with chON and 33 eyes (30 patients) with glaucoma, we investigated the change in cup-to-disc (C/D) area from the baseline to overt cupping using flicker analysis. Compared to the baseline, 23 eyes (74.2%) had increased cup size and 3 (9.7%) had vascular configuration changes in the chONgroup; in contrast, all glaucoma eyes exhibited changes in cup size and vascular configuration. The increase in C/D area ratio was significantly smaller in chON (0.04 ± 0.04) compared to glaucoma (0.10 ± 0.04, P < 0.001); the minimum residual neuroretinal rim width showed a more pronounced difference (29.7 ± 8.2% vs 7.1 ± 3.9%, P < 0.001). The changes distributed predominantly towards the nasal direction in chON, contrasting the changes to the arcuate fibers in glaucoma. In conclusion, our results provide the first longitudinal evidence of true pathological cupping in chONcompared to photographically disease-free baseline. The marked difference in the residual minimum rim width reaffirms the importance of rim obliteration in the differential diagnosis between the two diseases.

Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Prostate cancer is one of the most common types of cancer in men. It usually grows slowly and may not show obvious symptoms at first. Prostate cancer can be diagnosed by symptoms or by a doctor performing certain tests during routine health checkups. These tests include physical examination, PSA (Prostate Specific Antigen) Test, biopsy, imaging techniques, and Gleason score. In addition, Fourier transform infrared spectroscopy (FT-IR) is an analysis method used for prostate cancer diagnosis. This study aims to demonstrate FT-IR spectroscopy as an alternative method to other diagnostic methods in the diagnosis of prostate cancer. The FT-IR spectroscopy method is used to examine the molecular structure of samples. For prostate cancer diagnosis, FT-IR spectroscopy can be used to identify molecular changes in prostate tissue and identify characteristics by which cancerous cells differ from healthy cells. FT-IR spectroscopy is based on spectral data obtained by exposing samples to infrared radiation. These spectral data are based on properties associated with the movements of the molecules contained in the samples, such as vibration, rotation and bending. Molecular changes caused by diseases such as prostate cancer may be evident in these spectral data. These changes can provide information about the presence or stage of cancerous cells. Data obtained using FT-IR spectroscopy is processed with statistical analysis methods. These analyses are used to identify molecular differences between cancerous and healthy prostate tissues. In this way, FTIR spectroscopy can help obtain sensitive and accurate results in the diagnosis of prostate cancer.

Machine learning-based predictive model for abdominal diseases using physical examination datasets

Abdominal ultrasound is a key non-invasive imaging method for diagnosing liver, kidney, and gallbladder diseases, despite its clinical significance, not all individuals can undergo abdominal ultrasonography during routine health check-ups due to limitations in equipment, cost, and time. This study aims to use basic physical examination data to predict the risk of diseases of the liver, kidney, and gallbladder that can be diagnosed via abdominal ultrasound. Basic physical examination data contain gender, age, height, weight, BMI, pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, fasting blood glucose (FBG), and uric acid—we established seven single-label predictive models and one multi-label predictive model. These models were specifically designed to predict a range of abdominal diseases. The single-label models, utilizing the XGBoost algorithm, targeted diseases such as fatty liver (with an Area Under the Curve (AUC) of 0.9344), liver deposits (AUC: 0.8221), liver cysts (AUC: 0.7928), gallbladder polyps (AUC: 0.7508), kidney stones (AUC: 0.7853), kidney cysts (AUC: 0.8241), and kidney crystals (AUC: 0.7536). Furthermore, a comprehensive multi-label model, capable of predicting multiple conditions simultaneously, was established by FCN and achieved an AUC of 0.6344. We conducted interpretability analysis on these models to enhance their understanding and applicability in clinical settings. The insights gained from this analysis are crucial for the development of targeted disease prevention strategies. This study represents a significant advancement in utilizing physical examination data to predict ultrasound results, offering a novel approach to early diagnosis and prevention of abdominal diseases.

Modification effect of receipt of diabetes care on the association between COVID-19 infection and HbA1c level during the first year of the coronavirus pandemic using a nationwide population-based database.

We assessed the modification effect of adherence to diabetes care on the association between HbA1c levels and the risk of coronavirus disease 2019 (COVID-19) among individuals with diabetes using a population-based database. We retrospectively identified individuals with diabetes during routine health checkups performed in 2019 in Japan using a population-based claims database (JMDC, Tokyo, Japan). We assessed the risk of COVID-19 infection in 2020 in relation to HbA1c levels during routine checkups, stratified by the presence/absence of follow-up for diabetes care in 2019. Several sensitivity analyses were performed. We identified 65,956 individuals with an HbA1c ≥6.5% and fasting glucose ≥126 mg/dL in routine checkups, including 52,637 and 13,319 with and without at least one physician consultation for diabetes care in 2019, respectively. Although high HbA1c levels were associated with an increased risk of COVID-19 infection in a dose-dependent manner among individuals without diabetes care in 2019 (odds ratios, 1.53 and 2.17 in individuals with HbA1c of 7.0-7.9% and ≥8.0%, respectively) with a reference to HbA1c of 6.5-6.9%, individuals with diabetes care had no such trend in 2019 (odds ratios, 0.99 and 0.97 among individuals with HbA1c of 7.0-7.9% and ≥8.0%, respectively). Sensitivity analyses yielded consistent results when the variable definitions were changed and after multivariable adjustment with multiple imputation. This population-based study suggests that adherence to diabetes care may modify the association between HbA1c levels and the risk of COVID-19 infection.

Interesting case report on Chiari network

The Chiari network is a congenital remnant that is typically found in the right atrium and is generally not associated with significant pathophysiological implications. Usually, this is detected on general routine health checkup screening or casual examination. But, occasionally in certain cases, it is possible for this condition to results in diagnostic ambiguity when assessing right atrial disorders. Additionally, it has potential to contribute to the development of thromboembolism by inducing flow blockage. The Chiari network can be accurately diagnosed by echocardiography. This network appear as a highly mobile, highly reflectant echo target at many regions in the right atrium during transthoracic and transesophageal echocardiography. In this study, we provide a case involving an 18 year old male students studying cardiac technology who was diagnosed incidentally during echocardiography practical classes with Chiari network, mild TR and mild PR. The value and precision of echocardiography are becoming more widely recognized and being used more frequently. The potential for finding normal anatomical variations and their potential incorrect interpretation as pathological states coincides with the wider implementation of this imaging method.

Subjective versus objective refraction in healthy young adults

PurposeTo evaluate objective and subjective refraction differences in healthy young adults.MethodsData concerning candidates for the Israeli Air Force Flight Academy, as well as active air force pilots in all stages of service who underwent a routine health checkup between the years 2018 and 2019 were retrospectively analyzed. Objective refraction measured using a single autorefractometer was compared with subjective refraction measured by an experienced military optometrist during the same visit. The results were converted to power vectors (spherical equivalent [SE], J0, and J45). To interpret astigmatism using power vector values, the cylinder power (Cp) was determined.ResultsThis study included 1,395 young adult participants. The average age was 22.17 years (range, 17–39, 84.8% males). The average SE was − 0.65 ± 1.19 diopter (D) compared with − 0.71 ± 0.91D in the auto- and subjective refraction, respectively (p = 0.001). Cp was 0.91 ± 0.52D and 0.67 ± 0.40D, respectively (p < 0.001). This difference was more common in older participants (p < 0.001). J0 and J45 value differences were not significant. The absolute SE value of subjective refraction was lower in the myopic (p < 0.001) and hyperopic (p < 0.001) patients.ConclusionsYoung hyperopic participants tended to prefer “less plus” in subjective refraction compared with autorefraction. Young myopic participants tended to prefer “less minus” in subjective refraction compared with autorefraction. All participants, but mainly older participants, preferred slightly “less Cp” than that measured using autorefraction; The astigmatic axis did not differ significantly between the methods.

LASSO-derived model for the prediction of lean-non-alcoholic fatty liver disease in examinees attending a routine health check-up

Background Lean individuals with non-alcohol fatty liver disease (NAFLD) often have normal body size but abnormal visceral fat. Therefore, an alternative to body mass index should be considered for prediction of lean-NAFLD. This study aimed to use representative visceral fat links with other laboratory parameters using the least absolute shrinkage and selection operator (LASSO) method to construct a predictive model for lean-NAFLD. Methods This retrospective cross-sectional analysis enrolled 2325 subjects with BMI < 24 kg/m2 from medical records of 51,271 examinees who underwent a routine health check-up. They were randomly divided into training and validation cohorts at a ratio of 1:1. The LASSO-derived prediction model used LASSO regression to select 23 clinical and laboratory factors. The discrimination and calibration abilities were evaluated using the Hosmer–Lemeshow test and calibration curves. The performance of the LASSO model was compared with the fatty liver index (FLI) model. Results The LASSO-derived model included four variables—visceral fat, triglyceride levels, HDL-C-C levels, and waist hip ratio—and demonstrated superior performance in predicting lean-NAFLD with high discriminatory ability (AUC, 0.8416; 95% CI: 0.811–0.872) that was comparable with the FLI model. Using a cut-off of 0.1484, moderate sensitivity (75.69%) and specificity (79.86%), as well as high negative predictive value (95.9%), were achieved in the LASSO model. In addition, with normal WC subgroup analysis, the LASSO model exhibits a trend of higher accuracy compared to FLI (cut-off 15.45). Conclusions We developed a LASSO-derived predictive model with the potential for use as an alternative tool for predicting lean-NAFLD in clinical settings.

Correlation Between Thyroid Profile and Glycated Haemoglobin (HbA1c) in Type 2 Diabetes Mellitus Patients

Diabetes mellitus is a global health problem affecting major populations worldwide. It is a group of metabolic disorders characterized by absolute or relative deficiencies in insulin secretion with chronic hyperglycaemia and disturbances of carbohydrate, lipid, and protein metabolism. Thyroid hormones are secreted from thyroid gland. Thyroid hormone replacement is associated with a decrease in Glycated Hemoglobin (HbA1c) level. The present study was undertaken to study correlation between thyroid profile and glycated Haemoglobin (HbA1c) in Type 2 Diabetes Mellitus patients. A hospital based case control study was carried out in Dept. of Biochemistry, North Bengal Medical College, Sirajganj, Bangladesh from July to December 2022. 100 participants were in the study group and 100 participants in control groups were enrolled by following procedure. Patients aged 30 years or more who came to routine health check-up were taken for the study. Basic information, clinical history was taken from the record files. 50 cases of Type 2 Diabetes mellitus and 50 healthy controls were taken. Blood samples were collected and analyzed for thyroid profile, HbA1c, Fasting Blood Sugar and Renal Function Test. We found that there was a positive correlation between TSH (Thyroid stimulating hormone) and HbA1c levels, and a negative correlation between Thyroid hormones [Triiodothyronine (T3) &amp; Thyroxine (T4)] and Glycated Haemoglobin (HbA1c) in cases of type 2 Diabetes Mellitus. There is a significant decrease in TT3 (Total Triiodothyronine) and TT4 (Total Thyroxine) and significantly increased TSH in cases of Diabetes mellitus as compare to healthy Controls. We found that out of 50 cases of type 2 Diabetes Mellitus, 31% patients showed thyroid disorders. There is a high prevalence of thyroid disorders in Diabetic patients. If not diagnosed early, it could negatively affect diabetes and its complications. Therefore, routine screening of thyroid profile in diabetic patients is advisable to improve quality of life and reduce morbidity rate.

A novel approach to quantifying individual's biological aging using Korea’s national health screening program toward precision public health

Accurate prediction of biological age can inform public health measures to extend healthy lifespans and reduce chronic conditions. Multiple theoretical models and methods have been developed; however, their applicability and accuracy are still not extensive. Here, we report Differential Aging and Health Index (DAnHI), a novel measure of age deviation, developed using physical and serum biomarkers from four million individuals in Korea's National Health Screening Program. Participants were grouped into aging statuses (< 26 vs. ≥ 26, < 27 vs. ≥ 27, …, < 75 vs. ≥ 75 years) as response variables in a binary logistic regression model with thirteen biomarkers as independent variables. DAnHI for each individual was calculated as the weighted mean of their relative probabilities of being classified into each older age status, based on model ages ranging from 26 to 75. DAnHI in our large study population showed a steady increase with the increase in age and was positively associated with death after adjusting for chronological age. However, the effect size of DAnHI on the risk of death varied according to the age group and sex. The hazard ratio was highest in the 50–59-year age group and then decreased as the individuals aged. This study demonstrates that routine health check-up biomarkers can be integrated into a quantitative measure for predicting aging-related health status and death via appropriate statistical models and methodology. Our DAnHI-based results suggest that the same level of aging-related health status does not indicate the same degree of risk for death.