14-01-2026
THIS IS AN ONLINE E LOG BOOK TO DISCUSS OUR PATIENT'S DE-IDENTIFIED HEALTH DATA SHARED AFTER TAKING HIS SIGNED INFORMED CONSENT. HERE WE DISCUSS OUR PATIENT'S PROBLEMS THROUGH SERIES OF INPUTS FROM AVAILABLE GLOBAL ONLINE COMMUNITY EXPERTS WITH AN AIM TO SOLVE THOSE PATIENT'S CLINICAL PROBLEMS WITH COLLECTIVE CURRENT BEST EVIDENCE BASED INPUTS.
[4.17 pm, 14/01/2026] PPM 1: He was brought to us for the first time in August 2022 when @PPM3 was intern and his EMR summary from then is pasted below.
Age/Gender: 38 Years/Male
Address:
Discharge Type: Relieved
Admission Date: 24/08/2022 03:53 AM
Discharge Date: 27/08/2022 11:12 AM
PGY3
PGY1
INTERN
INTERN
INTERN
Diagnosis
SEIZURES SECONDARY TO NON KETOTIC HYPERGLYCEMIA WITH UNCONTROLLED
DIABETES MELLITUS
DENOVO HTN WITH HYPERTENSIVE RETINOPATHY.
Case History and Clinical Findings
This is a case of 38 year old male, cable operator by occupation has presented to the casualty with
the chief complaints of Seizures since yesterday night
HOPI
The patient is apparently asymptomatic 5 years back. He was diagnosed to be type 2 dm and was using
metformin 500mg OD ever since.
His last checkup for sugars was around 5 to 6 months ago
2DAYS AGO
Headache since 2 days associated with generalised weakness
YESTERDAY NIGHT
Lower and upper limb pain, Stiffness, Deviation of mouth towards left side, Involuntary movements for 20 to 30 sec first started in the left side and then became generalised
Frothing and tongue biting
Loss of consciousness for 3 min
Post ictal confusion for 5 min
Similar episodes 4 (2at home and 2 in ambulance)
Past history
Known case of diabetes since 5 years on metformin Po OD
Not a known case of HTN/ASTHMA/TB/CAD
PERSONAL HISTORY
DIET mixed
APETITE decreased since 1 week
BOWEL AND BLADDER MOVEMENTS normal
SLEEP adequate
ADDICTIONS alcoholic occasional
FAMILY HISTORY
not significant
GENERAL EXAMINATION The patient is conscious coherent but not cooperative well oriented to
time place and person. He is moderately built and nourished
Pallor absent
Icterus absent
Cyanosis absent
Clubbing absent
Edema absent
Lymphadenopathy absent
Vitals
Temp afebrile
PR 82bpm
RR 18 cpm
GRBS
10am- 377mg/dl 6ml/hr
2pm- 152mg/dl 2ml/hr
6pm-170mg/dl 2ml/hr
8pm- 96mg/dl 2ml/hr
2am 84mg/dl
SYSTEMIC EXAMINATION
CVS-s1s2 +
Pa-soft and non tender
Rs-BAE+
Investigation
HEMOGRAM
24/8/2022
HB-15.8
TLC-13200
PCV-44.7
MCV-88
MCH-31.1
MCHC-35.3
PLATELETS- 1.64
RBC-5.08
25/8/2022
HB-14.4
TLC-11000
PCV-40.7
MCV-89.3
MCH-31.6
MCHC-35.4
PLATELETS- 3.70
RBC-4.56
26/8/2022
HB-14.7
TLC-8900
PCV-42.8
MCV-91.5
MCH-31.4
MCHC-34.3
PLATELETS- 3.51
RBC-4.68
27/8/2022
HB-14.4
TLC-7300
PCV-41.2
MCV-90.9
MCH-31.6
MCHC-34.8
PLATELETS-3.45
RBC-4.54
ECG-NORMAL SINUS RHYTHM
MRI BRAIN-(24/8/2022)
POSTERIOR PREDOMINANT SUBCORTICAL FLAIR HYPOINTENSITY AND CORTICAL(GYRAL)
HYPERINTENSITY IN BILATERAL CEREBRAL HEMISPHERES, MORE ON LEFT SIDE
F/S/O HYPERGLYCEMIA INDUCED SEIZURES/NON KETOTIC HYPERGLYCEMIA
BILATERAL SYMMETRICAL PERIVENTRICULAR HYPERINTENSITIES-CHRONIC SMALL
VESSEL ISCHEMIA
USG-NO SONOLOGICAL ABNORMALITY DETECTED
FUNDOSCOPY-MILD TO MODERATE CHANGES OF HYPERTENSIVE RTINOPATHY NOTED
Treatment Given (Enter only Generic Name)
1) inj HAI 1ml (400)+39 ml NS2)inj levipil 500mg iv bd3)inj thiamine 200mg in 100ml NS iv TID4) inj zofer 4mg/iv/sos5) inj pan 40 mg iv OD6) BP/pr/rr/spo2 charting 2nd hourly8) tab atorvas 20 mg Po OD
Page-5
KIMS HOSPITALS
Advice at Discharge
1)TAB LEVIPIL 500MG PO BD
2)TAB TELMA 40MG PO OD
3)INJ HAI 6U SC TID
4 INJ NPH 6 UNITS SC BD
4)STRICT DIABETIC DIET AND SALT RESTRICTION
Follow Up
REVIEW SOS
When to Obtain Urgent Care
IN CASE OF ANY EMERGENCY IMMEDIATELY CONTACT YOUR CONSULTANT DOCTOR OR
ATTEND EMERGENCY DEPARTMENT.
Preventive Care
AVOID SELF MEDICATION WITHOUT DOCTORS ADVICE, DONOT MISS MEDICATIONS. In case of Emergency or to speak to your treating FACULTY or For Appointments, Please Contact: For Treatment Enquiries Patient/Attendant Declaration: - The medicines prescribed and the advice regarding preventive aspects of care, when and how to obtain urgent care have been explained to me in my own language
SIGNATURE OF PATIENT /ATTENDER
SIGNATURE OF PG/INTERNEE
SIGNATURE OF ADMINISTRATOR
SIGNATURE OF FACULTY
Discharge Date: 27/8/2022Ward: Unit:2
[4:19 pm, 14/01/2026] PPM 1: His creatinine was 1.4-2.0 during his first admission in 2022 and gradually progressed till 2026 to 11 and he came to us recently with shortness of breath suggestive of pulmonary edema
[4:17 pm, 14/01/2026] PPM 1: @PPM4 please share his deidentified chest X-ray and ECG
[6.48 pm,14/01/2026] PPM 2: Suggests SIRD though. @PaJR Health review the literature and tell us about SIRD type of diabetes.
[6:49 pm, 14/01/2026] PaJR Health: SIRD (Severe Insulin-Resistant Diabetes) is recognized as a distinct subtype of Type 2 Diabetes, characterized by significant insulin resistance. Research indicates that individuals classified with SIRD have a higher risk of developing specific complications.
Here's a summary from the literature:
* *Characteristics:* SIRD is identified through clinical parameters, including measures of insulin resistance. Patients in this subtype exhibit low whole-body insulin sensitivity and high fasting adipose-tissue insulin resistance.
* *Associated Complications:* The SIRD group has been found to have an increased risk for diabetic kidney disease (DKD) and fatty liver. Studies also show a higher prevalence of hepatic fibrosis in SIRD patients compared to other diabetes subtypes.
This subclassification of diabetes, including SIRD, is important for personalized medicine approaches, allowing for focused clinical resources and tailored treatment strategies to manage specific risks.
*References:*
* Subtypes of Type 2 Diabetes Determined From Clinical Parameters. [PMID: 32843567](https://pubmed.ncbi.nlm.nih.gov/32843567/)
* Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. [PMID: 31345776] (https://pubmed.ncbi.nlm.nih.gov/31345776/)
* Machine learning-based reproducible prediction of type 2 diabetes subtypes. [PMID:39168869](https://pubmed.ncbi.nlm.nih.gov/39168869/)
[6.54 pm, 14/01/2026] PPM 1:👍
[6:54 pm, 14/01/2026] PaJR Health: Great! I'm glad that information was helpful. The SIRD subtype is a fascinating development in diabetes research, moving us toward more personalized care. If you'd like to explore this further—perhaps looking at how many cases in our local database might fit this profile, or comparing treatment approaches for insulin-resistant diabetes—just let me know!
Recent ECG Recent chest X-ray
[4:31 pm, 15/01/2026] PPM 2: Save this to this patient's record. When you say severe insulin resistance - how can patients measure insulin resistance at home? What are sensitive and specific and reliable clinical markers of insulin resistance? Share data
[4:31 pm, 15/01/2026] PaJR Health: I have saved the information about the patient potentially having Severe Insulin-Resistant Diabetes (SIRD) to their record.
*MEASURING INSULIN RESISTANCE AT HOME*
📍 Currently, there isn't a direct, highly accurate method for patients to measure insulin resistance at home. Clinical assessment typically involves blood tests and sometimes more specialized procedures.
*SENSITIVE, SPECIFIC, AND RELIABLE CLINICAL MARKERS OF INSULIN RESISTANCE*
Several clinical markers are used to assess insulin resistance, often derived from routine blood tests. These markers vary in their sensitivity, specificity, and reliability:
🔹 *Triglyceride-Glucose (TyG) Index*
💡 This index is considered a promising, simple, and cost-effective biomarker for insulin resistance due to its high sensitivity and specificity. It is calculated using fasting triglyceride and fasting glucose levels.
💡 Combining the TyG index with obesity indicators like waist circumference (TyG-WC) or waist-to-height ratio (TyG-WHtR) can further enhance its predictive performance for conditions like non-alcoholic fatty liver disease (NAFLD) and mortality in metabolic syndrome.
📍 *References:*
* Triglyceride glucose (TyG) index: A promising biomarker for diagnosis and treatment of different diseases. - https://pubmed.ncbi.nlm.nih.gov/39510865/
* Association between triglyceride-glucose related indices with the all-cause and cause-specific mortality among the population with metabolic syndrome. - https://pubmed.ncbi.nlm.nih.gov/38658993/
* Fatty liver biomarkers and insulin resistance indices in the prediction of non-alcoholic fatty liver disease in Ghanaian patients. - https://pubmed.ncbi.nlm.nih.gov/37814510/
* The association between novel metabolic parameters and all-cause/cardiovascular mortality in patients with metabolic syndrome is modified by age. - https://pubmed.ncbi.nlm.nih.gov/40022121/
* Relationship between insulin resistance surrogate markers with diabetes and dyslipidemia: A Bayesian network analysis of Korean adults. - https://pubmed.ncbi.nlm.nih.gov/40341273/
🔹 *Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)*
💡 HOMA-IR is an established marker calculated from fasting insulin and fasting glucose levels. While reliable, some studies suggest the TyG index may offer more comprehensive predictive capabilities for both diabetes and dyslipidemia.
📍 *References:*
* HOMA indices as screening tests for cystic fibrosis-related diabetes. - https://pubmed.ncbi.nlm.nih.gov/34090803/
* Relationship between insulin resistance surrogate markers with diabetes and dyslipidemia: A Bayesian network analysis of Korean adults. - https://pubmed.ncbi.nlm.nih.gov/40341273/
🔹 *Triglyceride-to-HDL Ratio (TG/HDL Ratio)*
💡 This ratio is another surrogate marker for insulin resistance, particularly useful in assessing dyslipidemia and can be a valuable screening tool, especially in non-obese men.
📍 *Reference:*
* Relationship between body adiposity parameters and insulin resistance: a preliminary study of surrogate markers in Japan. - https://pubmed.ncbi.nlm.nih.gov/38396110/
* Relationship between insulin resistance surrogate markers with diabetes and dyslipidemia: A Bayesian network analysis of Korean adults. - https://pubmed.ncbi.nlm.nih.gov/40341273/
🔹 *Inflammatory Markers*
💡 Chronic low-grade inflammation is closely linked to insulin resistance. Markers such as high-sensitive C-reactive protein (hsCRP) and Tumor Necrosis Factor-alpha (TNF-alpha) are associated with insulin resistance. TNF-alpha, in particular, has shown a stronger correlation with pregnancy-associated insulin resistance than hsCRP.
💡 Other inflammatory proteins, like IL-1 and TNF-receptor superfamily members, have also been identified as markers that can distinguish between insulin-resistant and insulin-sensitive individuals with metabolic syndrome.
📍 *References:*
* Inflammatory markers and cardiometabolic diseases. - https://pubmed.ncbi.nlm.nih.gov/26103538/
* Correlation between inflammatory markers and insulin resistance in pregnancy. - https://pubmed.ncbi.nlm.nih.gov/25111421/
* Inflammatory Markers Associated With Diabetes Mellitus - Old and New Players. - https://pubmed.ncbi.nlm.nih.gov/33238871/
* Novel Proteome Targets Marking Insulin Resistance in Metabolic Syndrome. - https://pubmed.ncbi.nlm.nih.gov/38931177/
🔹 *Adipocytokines and Other Hormones*
💡 Adiponectin and leptin, secreted by adipocytes, play roles in metabolic regulation. Dysfunction in their secretion is associated with insulin resistance.
💡 Sex Hormone-Binding Globulin (SHBG) levels have shown a negative correlation with insulin resistance, and decreased SHBG can contribute to conditions like polycystic ovary syndrome (PCOS) which is often linked to insulin resistance.
📍 *References:*
* Adipocytokines and metabolic syndrome. - https://pubmed.ncbi.nlm.nih.gov/15968578/
* Sex Hormone-Binding Globulin (SHBG) as an Early Biomarker and Therapeutic Target in Polycystic Ovary Syndrome. - https://pubmed.ncbi.nlm.nih.gov/33139661/
* Adiponectin, Leptin, and Insulin-Pathway Receptors as Endometrial Cancer Subtyping Markers. - https://pubmed.ncbi.nlm.nih.gov/29297146/
🔹 *Emerging Biomarkers*
💡 *N-lactoyl amino acids (Lac-AA):* These are emerging as potential integrative biomarkers for metabolic dysfunction and diabetic complications, showing strong positive correlations with markers of poor glycemic control.
💡 *Erythrocyte Metabolite Alterations:* Metabolite changes in erythrocytes have been demonstrated as reliable and sensitive biomarkers for disentangling the metabolic complexity of insulin resistance and childhood obesity.
💡 *Cystatin C:* This marker is upregulated in obesity and appears to counteract inflammation in peripheral insulin-sensitive tissues, thereby improving glucose metabolism.
💡 *MG53 (TRIM72):* This protein acts as a glucose-sensitive myokine/cardiokine that can induce systemic insulin resistance. Targeting circulating MG53 is being explored as a therapeutic avenue.
📍 *References:*
* N-lactoyl amino acids are potential biomarkers for insulin resistance and diabetic complications. - https://pubmed.ncbi.nlm.nih.gov/40693359/
* Probing erythrocytes as sensitive and reliable sensors of metabolic disturbances in the crosstalk between childhood obesity and insulin resistance: findings from an observational study, in vivo challenge tests, and ex vivo incubation assays. - https://pubmed.ncbi.nlm.nih.gov/39261864/
* Obesity-Induced Increase in Cystatin C Alleviates Tissue Inflammation. - https://pubmed.ncbi.nlm.nih.gov/32616516/
* Glucose-Sensitive Myokine/Cardiokine MG53 Regulates Systemic Insulin Response and Metabolic Homeostasis. - https://pubmed.ncbi.nlm.nih.gov/30586741/
It's important to note that while these markers are valuable, the gold standard for directly measuring insulin sensitivity remains the hyperinsulinemic-euglycemic clamp technique, which is a research-intensive procedure not typically used in routine clinical practice.
[4:34 pm, 15/01/2026] PPM 2: Sorry but none of these align with the model of PaJR - PaJR is patient journey record and PaJR is end user aka patient driven healthcare. Healthcare should not burden patients more than the disease itself. Just tell us how do sarcopenia and abdominal circumference measure for insulin resistance? Think at a popultion level where it is extremely easy for patients to follow!
[4:35 pm, 15/01/2026] PaJR Health: I understand your emphasis on patient-driven healthcare and easy-to-follow measures. You're right, the goal is to empower patients without adding undue burden. Let's look at sarcopenia and abdominal circumference as indicators for insulin resistance, keeping a population-level, easy-to-track approach in mind.
*SARCOPENIA AND INSULIN RESISTANCE*
Sarcopenia, the age-related loss of skeletal muscle mass, strength, and function, is strongly linked to insulin resistance. Skeletal muscle is a primary site for glucose uptake, so a reduction in muscle mass can directly impact how the body handles glucose.
* *How patients can measure it (Population Level):* While precise diagnosis of sarcopenia often involves specialized scans (like DXA), simple, practical ways to monitor muscle health at home or in community settings include:
* *Grip Strength:* Using a hand dynamometer (though this might require a clinic visit for initial measurement and calibration, patients could track changes over time).
* *Walking Speed:* Measuring the time it takes to walk a certain distance (e.g., 4 meters) can be a good indicator of physical function.
* *Self-assessment of functional decline:* Noticing difficulty with daily activities like rising from a chair, climbing stairs, or carrying groceries.
* *Clinical Relevance:* Studies show that low muscle mass and strength are associated with an increased risk of developing Type 2 Diabetes and worsening insulin resistance. The combination of sarcopenia and obesity (sarcopenic obesity) further exacerbates insulin resistance and increases the risk of adverse health outcomes, including cardiovascular disease and cognitive impairment.
*ABDOMINAL CIRCUMFERENCE (WAIST CIRCUMFERENCE) AND INSULIN RESISTANCE*
Abdominal circumference, or waist circumference, is a very straightforward and accessible measure that patients can track at home. It's a key indicator of central adiposity (fat around the abdomen), which is closely associated with insulin resistance.
* *How patients can measure it (Population Level):*
* *Simple Tape Measure:* Patients can easily measure their waist circumference using a standard tape measure. The measurement should be taken around the bare abdomen, just above the hip bone.
* *Clinical Relevance:*
* *Strong Correlation:* Increased waist circumference is a well-established indicator of visceral fat accumulation, which is metabolically active and contributes significantly to insulin resistance and chronic inflammation.
* *TyG-WC and TyG-WHtR:* Combining waist circumference with the Triglyceride-Glucose (TyG) index (which requires blood tests for triglycerides and glucose) creates more powerful predictive tools for insulin resistance and related conditions like sarcopenic obesity and non-alcoholic fatty liver disease (NAFLD). These combined indices are considered highly sensitive and specific.
* *Predictive Value:* High levels of these combined indices are significantly associated with the incidence of sarcopenic obesity and increased risk of stroke in middle-aged and older populations.
*SUMMARY FOR PATIENT-DRIVEN HEALTHCARE*
For patients, tracking *waist circumference* regularly is a simple, non-invasive, and highly relevant measure for monitoring central adiposity, a key driver of insulin resistance. While direct sarcopenia measurement can be more complex, being mindful of *changes in physical function and strength* (e.g., difficulty with daily tasks, reduced grip strength if a dynamometer is available) can provide valuable early indicators.
These accessible measures, especially when combined with periodic clinical assessments that include blood tests for markers like fasting glucose and triglycerides (to calculate the TyG index), can empower patients to actively participate in monitoring their metabolic health and identifying potential insulin resistance at an early stage.
*References:*
* Longitudinal analysis of insulin resistance and sarcopenic obesity in Chinese middle-aged and older adults: evidence from CHARLS. - https://pubmed.ncbi.nlm.nih.gov/39624421/
* Obesity, Sarcopenia, Sarcopenic Obesity, and Hypertension: Mediating Role of Inflammation and Insulin Resistance. - https://pubmed.ncbi.nlm.nih.gov/39918389/
* Insulin resistance and sarcopenia: a prognostic longitudinal link to stroke risk in middle-aged and elderly Chinese population. - https://pubmed.ncbi.nlm.nih.gov/39385146/
* Sarcopenia: An Age-Related Multifactorial Disorder. - https://pubmed.ncbi.nlm.nih.gov/35249518/
* Sarcopenia and type 2 diabetes mellitus: a bidirectional relationship. - https://pubmed.ncbi.nlm.nih.gov/31372016/
* Sarcopenic obesity and insulin resistance: Application of novel body composition models. - https://pubmed.ncbi.nlm.nih.gov/32279031/
* Sarcopenia: a potential cause and consequence of type 2 diabetes in Australia's ageing population? - https://pubmed.ncbi.nlm.nih.gov/27681976/
* Management of Type 2 Diabetes Mellitus in Elderly Patients with Frailty and/or Sarcopenia. - https://pubmed.ncbi.nlm.nih.gov/35886528/
* Pathogenesis and comprehensive treatment strategies of sarcopenia in elderly patients with type 2 diabetes mellitus. - https://pubmed.ncbi.nlm.nih.gov/38260146/
* Body Composition, Sarcopenic Obesity, and Cognitive Function in Older Adults: Findings From the National Health and Nutrition Examination Survey (NHANES) 1999-2002 and 2011-2014. - https://pubmed.ncbi.nlm.nih.gov/38564377/
* Metabolic dysfunction-associated steatotic liver disease: A story of muscle and mass. - https://pubmed.ncbi.nlm.nih.gov/40495947/
* Comparing the effectiveness of different indicators of insulin resistance in predicting diabetes among adults with sarcopenia from the NHANES (1999-2018). - https://pubmed.ncbi.nlm.nih.gov/41088681/
[4:41 pm, 15/01/2026] PPM 2: Excellent. The first two references you cite appear relevant. Can you tell us more and give us absolute numbers from both studies and identify what was measured?
[4:41 pm, 15/01/2026] PaJR Health: Here's a closer look at the first two studies you highlighted, focusing on the absolute numbers and measurements used:
*1. Longitudinal analysis of insulin resistance and sarcopenic obesity in Chinese middle-aged and older adults: evidence from CHARLS.*
📍 [PMID: 39624421](https://pubmed.ncbi.nlm.nih.gov/39624421/)
* *Population Studied:* 6,395 Chinese middle-aged and older adults from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2015.
* *Measurements:*
* *Insulin Resistance (IR) Surrogates:* Six different indices were assessed:
* Triglyceride-glucose (TyG) index
* TyG-waist circumference (TyG-WC)
* TyG-waist-to-height ratio (TyG-WHtR)
* Triglyceride-to-high-density lipoprotein-cholesterol ratio (TG/HDL)
* Metabolic score for insulin resistance (METS-IR)
* Chinese visceral adiposity index (CVAI)
* *Sarcopenic Obesity (SO):* Defined based on specific criteria (details not provided in the abstract, but typically involves low muscle mass and high adiposity).
* *Absolute Numbers/Key Findings:*
* Over a 4-year follow-up, *319 participants (5.0%)* developed sarcopenic obesity.
* All six IR surrogates were significantly associated with sarcopenic obesity.
* The adjusted odds ratios (ORs) for sarcopenic obesity per standard deviation increase in the indices were:
* TyG: *1.21* (95% CI: 1.08-1.36)
* TyG-WC: *1.56* (95% CI: 1.39-1.75)
* TyG-WHtR: *2.04* (95% CI: 1.81-2.31)
* TG/HDL: *1.11* (95% CI: 1.01-1.21)
* METS-IR: *1.67* (95% CI: 1.50-1.87)
* CVAI: *1.74* (95% CI: 1.55-1.97)
* TyG-WHtR was identified as potentially the most appropriate indicator for predicting sarcopenic obesity in this population.
*2. Obesity, Sarcopenia, Sarcopenic Obesity, and Hypertension: Mediating Role of Inflammation and Insulin Resistance.*
📍 [PMID: 39918389](https://pubmed.ncbi.nlm.nih.gov/39918389/)
* *Population Studied:* 183,091 participants from the UK Biobank, a large-scale prospective cohort.
* *Measurements:*
* *Obesity:* Defined using percentage of fat mass.
* *Sarcopenia:* Defined as low muscle mass and low muscle strength.
* *Sarcopenic Obesity:* Concomitant presence of low muscle mass and high adiposity.
* *Hypertension:* New-onset hypertension within a 5-year follow-up.
* *Mediating Factors:* Inflammation indicators (C-reactive protein, neutrophils, white blood cells) and insulin resistance markers (triglyceride-glucose index, triglyceride-to-high-density lipoprotein cholesterol ratio).
* *Absolute Numbers/Key Findings:*
* During 5 years of follow-up, *3,812 participants (2.08%)* developed hypertension.
* Compared to men without these conditions, men with:
* Obesity had *2.32 times* higher risk of hypertension (95% CI, 2.12-2.55).
* Sarcopenia had *3.10 times* higher risk of hypertension (95% CI, 2.35-4.08).
* Sarcopenic obesity had *3.66 times* higher risk of hypertension (95% CI, 2.98-4.50).
* Compared to women without these conditions, women with:
* Obesity had *2.27 times* higher risk of hypertension (95% CI, 2.03-2.54).
* Sarcopenia had *2.93 times* higher risk of hypertension (95% CI, 1.95-4.39).
* Sarcopenic obesity had *4.04 times* higher risk of hypertension (95% CI, 3.32-4.91).
* Inflammation and insulin resistance markers (TyG index and TG/HDL ratio) mediated the association between sarcopenic obesity and hypertension, with mediations ranging from *6% to 13% for men* and *2% to 21% for women*.
