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Translational Metabolism Research Laboratory Improving human health through translational metabolism research. In the Clinic We seek real answers to critical metabolic health issues by studying real people.

In the Laboratory We investigate the molecular and cellular basis of metabolic disease and lifestyle interventions. News and awards Image. Public Health Insider — Webcast Understand your metabolism — and learn how to take control of it.

Congratulations Graduates Congrats to Mike Murphy on completing his undergraduate degree in Kinesiology. New research grant The TMRL received funding from the Collins Medical Trust to test the interaction between a drug for type 2 diabetes and exercise.

Student Award Doctoral Student Erin McGowan was selected as a top student presenter at the International Biochemistry of Exercise Conference. Students Present Research Doctoral Students Erin McGowan and Phil Batterson presented their research at the International Biochemistry of Exercise Conference.

Enabled by robotic accessioning technologies and a LIMS sample tracking system, the Biorepository can accommodate high and low throughput sample processing for the institute and long-term storage of research samples. The wet laboratory enables advanced cellular, molecular, microscopy, functional assessments including high-resolution measurements of metabolism and circulating metabolites from clinical samples.

Daily operations are coordinated by a dedicated team of nutrition professionals, who are actively involved in the design and implementation of research proposals, protocols, and procedures at the TRI-MD. This team works in conjunction with the Clinical Research Unit and Calorimetry Cores to provide precise dietary interventions for research studies with nutritional factors under investigation.

The Metabolic Kitchen serves as a centralized location for the preparation and storage of research-specific meal components, with a wide variety of commercial-grade stainless steel cooking equipment and walk-in cooler and freezer space. The institute is staffed with a master's-prepared Informatics Solutions Lead for the collection, quality assessment, storage and analysis of study data.

It is supported by a Biostatistical core and full-time Bioinformatician for the assessment of large and complex clinical data streams. The TRI occupies a custom-designed 54, sq.

translational research facility located on the AdventHealth Orlando campus. First Floor: The Clinical Operations Core has a comfortable waiting area, nine exam rooms, medical records space, participant check in and consenting areas, an area dedicated to anthropometrics, ECG and vital sign measurements, a phlebotomy room with two phlebotomy chairs, sample processing laboratory equipment and offices for clinical study coordinators.

Immediately adjacent to the clinic are the faculty and administrative offices, the Imaging Core and two conference rooms. Second Floor: Dedicated to advanced clinical phenotyping, contains the Clinical Research Unit, the Metabolic Kitchen, Calorimetry Core, the Laboratory, and Research Pharmacy.

Third Floor: The Exercise and Bioenergetics Laboratory includes exercise testing and training equipment, an exercise testing and biopsy suite and a laboratory for biochemical analysis of tissue, including muscle specimens.

To be completed in , the third floor will also add approximately sq. of laboratory space for advanced analysis of human biospecimens. On October 27, , the Translational Research Institute celebrated a Decade of Discovery - 10 years of conducting research. For more information, please click A Decade of Discovery.

Translational Research. Featured Clinical Trials. View All Clinical Trials. Meet the Team. Meet the Entire Team. Our Capabilities Research Participant Recruiting. Research Clinic. Clinical Research Unit. Exercise Testing and Training. Metabolic Kitchen.

Data Management, Statistics and Bioinformatics. About the Facility The TRI occupies a custom-designed 54, sq.

All individuals included in the database were aged between 40 and 79 years in , followed up through The cohort data contain death, healthcare usage, and health screening information. The variables from the NHIS were income-based insurance premium a proxy for house income , demographic variables, date of death, cause of death, prescription records, and disease diagnosis codes.

Korean NHIS provides NHSPs biennially. This cohort followed from to ; however, NHSPs measured lipid profile parameters such as serum triglyceride TG , HDL cholesterol HDL-C , and low-density lipoprotein cholesterol LDL-C levels since ; therefore, we set and as the baseline, and followed up to Figure 1 presents this study's inclusion and exclusion criteria.

Participants were sequentially excluded based on the criteria mentioned above, which were not mutually exclusive. After full exclusion, , participants 85, men and 66, women were included in the final analysis.

The ethics committee of NHIS waived the need for informed consent because the data from the NHIS-HEALS were anonymized at all stages, including during data cleaning and statistical analysis. The Institutional Review Board of the Chungbuk National University Hospital approved the present study CBNUH , which adhered to the principles of the Declaration of Helsinki Participants were divided according to the presence of MetS and BMI categories for each sex.

The metabolically unhealthy group MUH included individuals diagnosed with MetS, while the metabolically healthy group MH did not. BMI was categorized into normal-weight, overweight, and obesity 13 : normal-weight NW , By combining MetS and BMI categories, all participants were assigned to one of the following six groups: MHNW, metabolically healthy and normal weight; MHO, metabolically healthy and overweight; MHO, metabolically healthy and obese; MUHNW, metabolically unhealthy and normal weight; MUHOW, metabolically unhealthy and overweight; MUHO, metabolically unhealthy and obese.

The NHSPs collected information regarding hypertension, family history of diabetes, smoking status, alcohol consumption, and physical activity from self-reported questionnaires. Smoking status was categorized as never smoker, former smoker, or current smoker.

Current smokers were defined as individuals who answered "Yes, and I currently smoke cigarettes. We categorized economic status into three groups by income-based insurance premium: low, 1—3rd deciles; middle, 4th—7th deciles; and high, 8th—10th deciles.

Residential areas were categorized using residential area codes for metropolitan areas and other regions. Seven cities were classified as metropolitan areas by adding Seoul, a special city, to the six metropolitan cities of Incheon, Daejeon, Gwangju, Daegu, Ulsan, and Busan.

The endpoint of this study was to compare the occurrence rates of CVDs and all-cause mortality in the metabolic healthiness and obesity groups after enrollment — The composite outcome is sum of all-cause mortality and incidence of CVDs. CVDs were defined when the main diagnosis II25 or II69 was recorded at least twice in outpatients or once in hospitalized patients.

CVDs included IHD II25 and CbVDs II69 based on ICD codes. CbVDs were further divided into ischemic, hemorrhagic, and other CbVDs according to the diagnosis code as follows: ischemic CbVDs were coded as I63 cerebral infarction , I65 occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction , and I66 occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction ; hemorrhagic CbVDs as I60 subarachnoid hemorrhage , I61 intracranial hemorrhage , and I62 other nontraumatic intracranial hemorrhage ; and other CbVDs as I64 stroke, not specified as hemorrhage or infarction , I67 other cerebrovascular diseases , I68 cerebrovascular disorders in diseases classified elsewhere , and I69 sequelae of cerebrovascular disease.

We conducted subgroup analyses for each IHDs, CbVDs, and all-cause mortality. The start date of the research was defined as the day of the first health examination between and For participants diagnosed with CVD between and , the research end date was the date of initial diagnosis of the disease.

In cases where the participant died before a diagnosis of diabetes was made, the end date was defined as the date of death. Similarly, in cases where the participants had not died or had not been diagnosed with diabetes during the study period, the end date was the latest date of the last outpatient clinic visit, last health screening, or last when the participants took the prescribed medication.

Analysis of variance ANOVA tests for continuous variables and chi-squared tests for categorical variables were used to check for group differences. To investigate the association between MetS, obesity, and composite outcomes all-cause mortality and incidence of CVDs , outcome-free survival rates were estimated and compared using the Kaplan—Meier method and log-rank test.

We built three Cox proportional hazard regression models after adjusting for age, smoking status, alcohol consumption status, physical activity, economic status, residence area, alanine aminotransferase ALT , and gamma-glutamyl transferase GGT. We performed subgroup analysis for each outcome IHDs, CbVDs including ischemic and hemorrhagic CbVD, and all-cause deaths.

The outcome-free survival rates were estimated using the Kaplan—Meier method. Statistical analyses were performed using the statistical package SAS enterprise version 7.

Not applicable. The ethics committee of National Health Insurance Service NHIS waived the need for informed consent because the data from the NHIS-HEALS were anonymized at all stages, including during data cleaning and statistical analysis.

The Institutional Review Board of the Chungbuk National University Hospital approved the present study CBNUH A total of , participants 85, men and 66, women included in this study, and the median follow-up duration was 9. Table 1 shows the baseline characteristics of the study population according to the combination of MetS and BMI.

Table 1 Within the same BMI category, the MH group was younger than the MUH group was. Waist circumference, SBP, fasting glucose, ALT, and GGT levels were lower in the MH group than in the MUH group.

In both sexes, TG levels were higher in the MUH groups than the MH groups, while HDL-C and LDL-C levels tended to be higher in the MH groups than the MUH groups. The proportion of current smokers was lower in the MH group than in the MUH group in both sexes.

Among men, the MH group drank less alcohol and engaged in more regular physical activity than the MUH group. Economic status was higher in the male MH group. However, females in the MUH group drank less alcohol and had a higher economic status than those in the MH group.

The incidence of DM, hypertension, and dyslipidemia was higher in the MUH group than in the MH group. Within each MH and MUH group, the more obese groups had higher SBP, total cholesterol, LDL-cholesterol, and ALT levels. Figure 2 shows the estimated cumulative incidence of the composite outcomes based on the Kaplan—Meier survival curve.

A total of 36, composite outcomes were observed, accounting for At the end of the follow-up period, the estimated cumulative incidences of composite outcomes were as follows: MHNW Cumulative incidents of composite outcome all-cause mortality and incidence of cardiovascular diseases according to metabolic healthy and obesity.

Figure 3 presents the results of the Cox proportional hazard regression models to examine the association between MetS, BMI category, and the incidence of composite outcomes. In the metabolically healthy group, the higher BMI group had the higher risk of composite outcomes.

The metabolically unhealthy group had a higher risk in any given BMI group. Cox proportional hazards regression models for composite outcome all-cause mortality and incidence of cardiovascular diseases. Subgroup analysis was conducted to investigate the association between MetS, BMI category, and each outcome IHDs, CbVDs, ischemic CbVD, hemorrhagic CbVD, and all-cause mortality Fig.

The risk of hemorrhagic CbVDs was not significantly associated with the six MetS and BMI categories combined. Full-adjusted Cox proportional hazards regression models for incidence of ischemic heart diseases, cerebrovascular diseases, and all-cause mortality.

Apart from to Model 3, additional analysis was performed to check the marginal effect, which is how the composite outcome and each outcome change when the degree of obesity changes in the MH group and the MUH group Model 4, Supplementary Table 2.

In the MH group, the HRs increased according to the increase in BMI overweight in MH, 1. Based on the Korean NHIS-HEALS data, this retrospective study demonstrated that the risk of composite outcome increased in the MHOW, MHO, and MUH groups compared to the MHNW group.

In particular, after stratifying the composite outcome into IHD, CbVD, and all-cause death, all MUH groups showed an increased risk of IHD, CbVD, and ischemic CbVD incidence in both sexes compared to the MHNW group.

Recently, a few studies have shown that MHO did not increase the risk of CVD incidence more than MHNW 16 ; however, in our research, the risk of composite outcome, IHDs, CbVDs, and ischemic CbVDs in MHO was higher in MHNW. All-cause mortality is lower in MHO than in MHNW in men but not significantly different between the two groups in women.

The risk of all-cause mortality increased even in MUHNW in both sexes. In many cases, MetS and obesity coexist, and both can contribute to CVD Accordingly, through the results from analyses of the marginal effect conducted to confirm the interaction between MetS and obesity, the HRs for the composite outcome of the MUH group was found to be approximately 1.

These results are not significantly different from those of previous studies 18 , 19 , 20 ; the CVD risk in the MUH group increased by 1. Given these results, although additional research is needed, the effects of obesity on CVD outcomes may vary depending on metabolic health.

Previous studies have shown that various factors influence the occurrence of the MUH phenotype, and age, alcohol consumption status, low level of physical activity, low education level, and smoking are thought to be factors In addition, compared with MUHO, the MHO group had a better quality diet with a high intake of fruits, whole grains, meat, and beans In our study, men showed similar results.

In contrast, among women, the MH group showed a higher percentage of moderate alcohol consumption and lower economic status, and the MUH group had a relatively healthier lifestyle.

These differences might be due to the MUH group's chance for early detection and treatment of the disease through regular check-ups or hospital visits according to the higher economic status and the possibility of healthy lifestyle changes to manage chronic conditions, such as refraining from alcohol consumption.

However, the mechanism by which metabolic unhealthiness or obesity influences CVD and death has not been elucidated. In a study by Kassi et al. Differences in fat distribution can also be explained as the cause of worse CVD outcomes in an MUH population. In the case of the MUHNW population, there is little adipose tissue in the gluteo-femoral region that can store excess fat.

Instead, as trunk fat mass increases, previous studies have asserted that CVD risk increases independently In addition, recently, Single nucleotide polymorphisms SNPs related to lipid metabolism or insulin or glucose metabolism were observed in the MUHO or MUHNW groups, suggesting that they may be associated with CVD outcome at the gene level and the phenotype of obesity or MUH 23 , In our study, the risk of all-cause mortality tended to decrease with increasing BMI in both sexes in MH groups Overweight in MH, 0.

Although not statistically significant, this trend is also seen in the MUH groups. Recently, particularly in MHO, a mechanism has been suggested that intrinsic healthy adipose tissue allows excess adiposity without adipocyte dysfunction.

However, there is a limitation in that the cohort data used in this study did not include data on inflammatory markers such as interleukin-6 and high-sensitivity C-reactive protein, muscle mass, body fat distribution, diet, and SNP.

Therefore, it was impossible to confirm a direct relationship between the following mechanisms and the results. There was an age difference between each group of at least 1.

Even though age was adjusted in all models, the residual effect of age-related hormonal change, metabolic derangement, and other risk factors should be considered as potential limitations when interpreting this study.

In addition, since we did not consider the contribution of each factor involved in metabolic unhealthiness to the CVD outcome, the actual risk may differ from these results. Furthermore, both MetS and obesity are likely to be transient at one point in time.

This study estimated the risk of disease incidence and death according to MetS and obesity only at baseline. The possibility of risk changes according to status changes was not reflected due to cohort data limitations. Some previous studies 28 , 29 showed that elevated fasting glucose and low HDL-C were associated with increased mortality, but the results did not include changes in the overall observation period.

Since the definition of MetS or metabolic unhealthiness is not yet clear, even in previous studies, each researcher conducted the analysis using different criteria. The results also differed depending on the criteria used. However, in our study, metabolic unhealthiness was defined by applying criteria for MetS that are familiar to the clinical field.

It has the strength of analyzing a long-term follow-up period of approximately 10 years for a group that can relatively represent Koreans. In addition, this study has the strength of subdividing and comparing groups according to the metabolic health of each obesity degree and additionally analyzing the interaction between obesity and metabolic health.

This study confirmed that metabolically unhealthy and increased BMI at a single time point could also affect the risk of CVD and death. Significantly, the risk of CVD and all-cause mortality was higher in metabolically unhealthy individuals with BMI within the normal range than in other groups.

Efforts in the clinical field are necessary for disease prevention and management. For composite outcome, high BMI and metabolic unhealthiness were associated with increased risk. Katie Ullman Phone: kathryn. ullman nyulangone. Research , Translational Medicine , Press Releases.

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This is a multi-site randomized controlled trial of a lifestyle intervention designed to reverse metabolic syndrome. The project enrolled persons with metabolic syndrome at 5 sites across the US including Geisinger. Learn more at www. Contact Us: ELM geisinger. The Early Intervention to Promote Cardiovascular Health of Mothers and Children multicenter trial aims to test the effectiveness of an intervention designed to promote cardiovascular health and address cardiovascular disparities in both mothers and children years old.

ENRICH is funded by the National Heart, Lung and Blood Institute in collaboration with several federal partners. Geisinger is part of the Penn State Clinical Center, one of seven clinical centers in the trial.

Contact Us: enrich geisinger. edu or Longitudinal Cohort Studies. Central Pennsylvania Rural Birth Cohort PI: Lisa Bailey-Davis, DEd, RD Dates: — present.

Child and Adolescent Trend CAT Data Registry PI: Lisa Bailey-Davis, DEd, RD Dates: — present. The Childhood and Adolescent Trend CAT data registry includes a subset of the Geisinger electronic health record EHR and collects data on , Geisinger patients with a pediatric BMI measured with a same day height and weight at age 20 years or younger between and refreshed annually or on-demand.

In August , the CAT cohort had a median age of 18 years min 0 years, max 45 years, mean: The CAT registry is a collection of EHR data demographics, vitals, encounters with diagnosis, problem list diagnosis, labs, social history, medication history, and referrals as well as custom data including adult BMI, CDC and WHO BMI percentiles, BMI Extended, Family Nutrition and Physical Activity FNPA and Early Healthy Lifestyles EHL behavior and home environment data, food insecurity, and youth Blood Pressure Percentiles.

PREVENT PI: Lisa Bailey-Davis, DEd, RD Dates: — present The goal of this prospective, longitudinal cohort study is to determine if the pediatric care redesign with evidence-based screening using the Family Nutrition and Physical Activity, FNPA, tool , patient-centered education, negotiated decision-making and health information technology to reverse childhood obesity clinical improvement initiative is improving the quality of care by examining process and outcome measures associated with parent-child exposure to PREVENT.

FNPA is collected as a patient-reported outcome measure at annual well-child visits for children aged 2 to 9 years , 2 to 12 years present and years beginning Findings from this longitudinal study are used to continuously improve the value of and quality of care for the primary prevention of pediatric obesity and related comorbidities.

Our team collaborates with National FNPA Leaders myfnpa. org to facilitate dissemination and implementation of the tool to clinical sites globally. This project engages community partners in 5 central Pennsylvania counties for an Upstream Planning Initiative to Improve Response to Child Maltreatment.

Community-engaged planning is focused on identifying solutions that will alter the health trajectories for patients and families affected by or at risk for child maltreatment.

Contact Us: unity geisinger. The project aims to create a Council of patients with maternal health lived experiences, maternal health clinicians and researchers, and maternal health community partners guided by a shared vision to improve maternal outcomes for patients of racial and ethnic minorities and rural residency.

Preventing Diabetes This project aims to prevent diabetes among a high-risk patient population with pre-diabetes. Efficacious treatments for weight management exist but are rarely discussed in primary care. Our project addresses a gap in care by making existing clinical tools easily available to primary care providers through training, electronic health record prompts, clinical decision support tools, and patient activation strategies.

In collaboration with their physician, patients choose a weight management program that best meets their needs and lifestyle with an understanding of expected outcomes. This project puts risk, treatment, and predicted outcome information at the fingertips of patients and physicians and will result in an improved patient experience, increased demand for weight management discussions, improved quality of care, and prevention of diabetes through modest weight loss.

Geisinger Rural Aging Study The Geisinger Rural Aging Study GRAS began in with more than 20, participants age 65 and older. The participants live in communities in rural northeastern and central Pennsylvania.

Their health and nutrition has been followed over time, providing a means to study nutrition and aging. Clinical trials Our team of Clinical Research Coordinators, Project Managers, Project Coordinators, and Research Assistants have extensive experience in conducting phase II and III intent-to-treat and cardiovascular outcome clinical trials in the therapeutic areas of non-alcoholic steatohepatitis, weight management, and metabolic syndrome.