Visceral fat

Visceral fat, also known as abdominal obesity, is the excessive accumulation of fat in the abdominal cavity and is an important predictor of cardiometabolic risk. It develops when subcutaneous adipose tissue lacks sufficient capacity to store additional energy (from food, for example), causing excess fat to accumulate in organs including the liver, skeletal muscle, and the heart[1].

This situation is often accompanied by an increase in free fatty acids, hypertriglyceridaemia, and elevated release of pro-inflammatory cytokines, which contribute to insulin resistance and atherosclerosis[2]. Within Synapse, we measure the amount of visceral fat using the ratio of waist circumference to height.

The cut-off values applied at Synapse are:

	Waist circumference / height
Underweight [3]	< 0.37
Excellent [4, 5]	0.37 - 0.52
Excess	0.52 - 0.6
High excess	> 0.6

Underweight

In addition to excess visceral fat, underweight is also metabolically relevant. In adults, underweight is defined as a BMI <18.5 kg/m²[72, 73, 74] . Synapse assesses underweight based on BMI.

The severity of underweight can be further specified according to the DSM-5[73]:

Severity	BMI (kg/m²)
Mild	17-18.5
Moderate	16-16.99
Severe	15-15.99
Extreme	<15

Underweight often indicates a chronic energy deficit and is associated not only with low body fat percentage but also with loss of muscle mass. Since skeletal muscle plays a crucial role in glucose uptake and insulin sensitivity[75], reduced muscle mass can negatively affect metabolism. Underweight also increases the risk of fatigue, reduced immune function, hormonal dysregulation, and reduced bone density[76].

Hepatic steatosis (MAFLD)

Hepatic steatosis can develop through an accumulation of triglycerides in hepatocytes and is a growing problem in modern society. Because hepatic steatosis is often associated with obesity and insulin resistance, the term Non-Alcoholic Fatty Liver Disease (NAFLD) is now being replaced by Metabolic Associated Fatty Liver Disease[6] (MAFLD).

This algorithm was developed to identify patients at risk of metabolically associated hepatic steatosis, in whom lifestyle interventions or further diagnostic investigations may be warranted. The risk of hepatic steatosis is determined by:

• Validated aggregate scores from the literature.
• Individual biomarkers demonstrably associated with hepatic steatosis.
• Lifestyle factors proven to contribute to the development of hepatic steatosis.

Aggregate scores

Several aggregate scores[7] have been described that offer added diagnostic value in identifying metabolically associated hepatic steatosis. Synapse integrates only scores with proven validity and aligns the selection with the available biomarkers from the blood test.

FLI

The Fatty Liver Index (FLI) is a long-established, well-validated parameter[8] for screening for hepatic steatosis. Multiple studies[9] have compared the FLI against various imaging techniques, including CT and fibroscans[10]. This makes it an accessible and reliable method for evaluating the degree of hepatic steatosis.

where and

• TG: Triglycerides (mg/dL)
• BMI: Body Mass Index (kg/m^2)
• GGT: Gamma-glutamyltransferase (U/L)
• Waist circumference: in centimetres

A higher FLI score indicates a greater likelihood of hepatic steatosis. Within Synapse, the following thresholds[11] are used to estimate the risk of hepatic steatosis:

	FLI
Low risk	< 30
Moderate risk	30 - 45
High risk	45 - 60
Very high risk	> 60

Individual biomarkers

In addition to the aggregate scores used to determine the risk of hepatic steatosis, individual biomarkers can also provide further insight into liver health. In this context, alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), and aspartate aminotransferase (AST) are important parameters.

ALT (GPT)

Alanine aminotransferase (ALT), also known as GPT, is an enzyme found primarily in liver cells. When liver damage or inflammation occurs, additional ALT is released into the bloodstream, meaning elevated values may indicate hepatic steatosis or other liver conditions[14, 15] .

At Synapse, the following cut-off values[15, 16, 17, 18, 19, 20] for ALT have been adopted:

Risk	ALT
	Female	Male
Low risk	< 17 U/L	< 26 U/L
Moderate risk	17-20 U/L	26-31 U/L
High risk	21-28 U/L	32-45 U/L
Very high risk	>28 U/L	>45 U/L

GGT

Gamma-glutamyltransferase (GGT) plays a role in glutathione metabolism and is also present in high concentrations in liver tissue. An elevation in GGT may indicate hepatic steatosis, biliary tract abnormalities, or excessive alcohol use[14, 15, 21] .

At Synapse, the following cut-off values[22, 23] have been adopted:

Risk	GGT
	Female	Male
Low risk	< 14 U/L	< 23 U/L
Moderate risk	14-18 U/L	23-31 U/L
High risk	19-28 U/L	32-45 U/L
Very high risk	>28 U/L	>45 U/L

Lifestyle factors

In addition to biochemical and clinical parameters, lifestyle also plays a crucial role in the development and progression of (metabolically associated) hepatic steatosis. Synapse therefore incorporates the following factors in the risk assessment:

Alcohol

Excessive alcohol use can strain the liver, cause inflammation, and increase the likelihood of hepatic steatosis. Even at moderate consumption levels, alcohol can contribute to triglyceride accumulation in the liver[24].

Liver-stressing food

A dietary pattern high in saturated fats, refined carbohydrates, and added sugars can place additional burden on the liver and promote fat accumulation. Dietary changes therefore form an important component of the prevention and management of hepatic steatosis[25].

Insulin sensitivity

Insulin resistance is often associated with obesity and increases the risk of hepatic steatosis[26, 27] . A healthy lifestyle and weight management can improve insulin sensitivity, which has a positive effect on liver health.

Liver fibrosis

In some patients with metabolically associated hepatic steatosis, persistent inflammation leads to activation of hepatic stellate cells and progressive formation of extracellular matrix[78, 81] . This results in liver fibrosis, where functional liver tissue is replaced by scar tissue. The degree of fibrosis is the most important prognostic determinant of liver-related complications and increased cardiovascular mortality.

Within Synapse, the risk of liver fibrosis is evaluated whenever signs of hepatic steatosis are present. Two internationally validated, non-invasive scores are used for this purpose[77, 79, 80] : the FIB-4 index and the MAFLD Fibrosis Score (NFS). Both are recommended as a first step in risk stratification of MASLD/MAFLD and have a high negative predictive value for ruling out advanced fibrosis.

FIB-4 index

The FIB-4 index[77, 79] is a validated, non-invasive score that combines age, transaminases, and platelet count to estimate advanced liver fibrosis:

A FIB-4 <1.3 has a negative predictive value of approximately 90% for ruling out advanced fibrosis[79]. Values above 2.67 require further investigation with imaging. The cut-offs applied at Synapse are:

Risk	FIB-4
Low risk	<1.3
Moderate risk	1.3-2.67
High risk	2.67-3.25
Very high risk	>3.25

MAFLD

The MAFLD Fibrosis Score (NFS)[77, 80, 82] is a second validated score that integrates age, BMI, glucose homeostasis, transaminases, platelet count, and albumin:

where IFG/diabetes = 1 if present, otherwise 0.

The NFS has a negative predictive value of approximately 88%[82]. The low threshold is age-dependent:

Risk	NFS
Low risk	< -1.455 (<65 years) or < 0.12 (≥65 years)
Moderate risk	-1.455 to 0.676
High risk	> 0.675

Metabolic dysregulation

The metabolic dysregulation score is based on metabolic syndrome, a cluster of five cardiometabolic risk factors. If three or more of these risk factors are present, the criteria for metabolic syndrome are met and there is a clearly elevated risk of cardiometabolic disease[28, 29] .

These five risk factors are:

Metabolic syndrome	Female	Male
Waist circumference	> 79cm	> 93cm
Elevated triglycerides	> 150mg/dL
HDL cholesterol	< 50 mg/dL	< 40 mg/dL
Blood pressure	> 130/85 mmHg
Fasting glucose	> 100 mg/dL

Because the presence of these risk factors indicates already advanced metabolic dysregulation, Synapse has adopted a more nuanced approach for the metabolic dysregulation score. By applying stricter cut-offs, we can intervene more precisely before metabolic syndrome becomes fully established.

Waist circumference

Waist circumference is a simple measure for assessing central obesity. An elevated waist circumference is associated with higher cardiometabolic risk. The cut-off values used in most guidelines are: >93 cm for men and >79 cm for women.

Since these standard values do not account for height, Synapse has opted for a more accurate assessment using the waist-to-height ratio[3, 4] .

	Waist circumference / height
Excellent [4, 5]	< 0.52
Excess	0.52 - 0.6
High excess	> 0.6

Triglycerides

Elevated triglycerides (TG) may indicate impaired fat metabolism and contribute to atherosclerosis. The commonly used cut-off value for elevated triglycerides is >150 mg/dL (1.7 mmol/L).

Because elevated cardiometabolic risks have also been described at lower cut-off values[30], Synapse has adopted the following thresholds:

	Triglycerides
Low risk	≤ 130 mg/dL
Moderate risk	130-150 mg/dL
High risk	150 - 200 mg/dL
Very high risk	> 200 mg/dL

HDL cholesterol

HDL cholesterol is known as 'good' cholesterol and protects the vessel wall by transporting excess cholesterol back to the liver. A low HDL level may indicate elevated cardiovascular risk. Common cut-off values are 40 mg/dL for men and 50 mg/dL for women.

To detect metabolic dysregulation at an early stage, Synapse has adopted the following cut-offs:

HDL	Female	Male
Low risk	≥ 55 mg/dL	≥ 50 mg/dL
Moderate risk	50-55 mg/dL	40-50 mg/dL
High risk	<50 mg/dL	<40 mg/dL

Systolic BP

Elevated systolic blood pressure is an important risk factor for cardiovascular disease. Within metabolic syndrome, a cut-off of 130 mmHg is generally used for systolic pressure.

To detect metabolic dysregulation at an early stage, Synapse has adopted the following cut-offs:

Categories [31]	SBP (mmHg)	DBP (mmHg)
Normal	≤120	≤80
Elevated	121–130	81–85
Stage 1 hypertension	131–140	86–90
Stage 2 hypertension	>140	>90

Glucose

Elevated fasting blood glucose (or pre-existing diabetes) is a core component of metabolic syndrome. A cut-off value of 100 mg/dL (5.6 mmol/L) is commonly used.

To detect metabolic dysregulation in time, Synapse has decided to take into account not only fasting glucose but also insulin resistance. The insulin resistance algorithm is described below.

Insulin resistance

Insulin resistance (reduced sensitivity to insulin in muscle, liver, and fat cells) is generally the first recognisable step in disrupted metabolic homeostasis. This process often goes unnoticed for years, while underlying the development of overweight, hepatic steatosis, prediabetes, and ultimately type 2 diabetes.

Synapse maps insulin resistance by combining blood biomarkers with lifestyle factors. This allows us to better estimate both the early onset and later progression. Depending on which blood values are available, different evaluation methods are applied.

Algorithm overview

The first step in evaluating a patient's glucose homeostasis is to estimate where they fall on the spectrum. For this, HbA1c and fasting plasma glucose concentration can be used.

	HbA1c	Glucose (mg/dL)
Normal	< 5.7%	≤ 100
Possible prediabetes		100-110
Prediabetes	5.7-6.5%	110-125
Diabetes	> 6.5%	> 125

Hemoglobin A1C

HbA1c (glycated haemoglobin) reflects the average blood glucose concentration over the preceding two to three months. Values above 6.5% confirm the diagnosis of diabetes[32]. Unfortunately, for prediabetes and insulin resistance there is no consensus[33] and different organisations use different thresholds.

HbA1c	ADA	NICE
Normal	< 5.7%	< 6%
Prediabetes	5.7 to 6.5%	6 to 6.5%
Diabetes	> 6.5%	> 6.5%

Furthermore, the physiology of red blood cells changes with age, meaning HbA1c values in older individuals[34] may naturally rise independently of any increase in blood glucose levels. It is therefore important to also take age into account when selecting HbA1c thresholds.

Based on the above, Synapse has adopted the following thresholds:

HbA1c	<65y	≥65y
Normal	< 5.7%	< 6%
Prediabetes	5.7 to 6.5%	6 to 6.5%
Diabetes	> 6.5%	> 6.5%

Glucose

If HbA1c has not been measured, Synapse assesses insulin resistance using fasting plasma glucose. A higher fasting glucose concentration indicates that the pancreatic β-cells can no longer compensate for reduced insulin sensitivity.

Repeated measurements above 126 mg/dL confirm the diagnosis of diabetes[32], but again there is no consensus on the correct cut-off value (100 mg/dL or 110 mg/dL) for prediabetes. Synapse has therefore adopted the following thresholds[32]:

	Fasting glucose
No (pre)diabetes	<100 mg/dL
Possible prediabetes	100-110 mg/dL
Prediabetes	110-125 mg/dL
Diabetes	≥ 126 mg/dL

Biomarkers for insulin resistance

Insulin resistance describes the state in which the pancreatic beta cells must produce additional insulin to compensate for reduced insulin sensitivity. Although this condition plays a crucial role in the onset of metabolic dysregulation and diabetes[35], it is difficult to determine insulin resistance accurately in clinical practice.

The gold standard, the euglycaemic clamp (a highly accurate but invasive laboratory procedure), is too costly and impractical for routine use, which means suboptimal markers must be used in primary care as a necessary compromise[36]. Synapse has chosen to use the aggregate scores below due to their well-established scientific basis for detecting insulin resistance.

HOMA

The Homeostatic Model Assessment (HOMA) index[37], also known as HOMA-IR, is a mathematical method that estimates insulin sensitivity (HOMA-S) and beta-cell function (HOMA-β) using fasting glucose and insulin values. This model has been validated against the euglycaemic clamp[38] and can indicate early insulin resistance up to fifteen years[36] before elevated fasting glucose becomes apparent[39].

• HOMA-IR = (fasting glucose [mmol/L]) × fasting insulin [mIU/L]/22.5)
• HOMA-β = (20% x fasting insulin) / (fasting glucose − 3.5)
• HOMA-S = 100% / HOMA-IR

HOMA-2[45] is a more comprehensive, non-publicly available variant of HOMA-IR that, thanks to its underlying non-linear model, is generally more accurate[46]. Although HOMA-IR and HOMA-2 have their limitations, multiple recent meta-analyses confirm that this index is a useful biomarker for early detection of metabolic disorders and other health problems[42, 43, 44] . Limitations include the requirement to fast strictly for more than twelve hours, pronounced sensitivity to acute factors such as stress, illness, or sleep, and lack of consensus on optimal cut-off values[35, 40, 41] .

If the laboratory holds a HOMA2 licence, Synapse uses those values; otherwise, Synapse calculates HOMA-IR directly. The following thresholds[47, 48, 49] apply:

	HOMA-IR[50]	HOMA-2-IR[50]
Low risk	≤ 2	≤ 1.4
Moderate risk	2 - 2.7	1.4 - 1.8
High risk	2.7 - 3.8	1.8 - 2.2
Very high risk	3.8 - 5	2.2 - 2.7
Extremely high risk	> 5	> 2.7

Trig / HDL

The triglyceride-to-HDL cholesterol ratio (Trig/HDL ratio) is a simple and cost-effective indicator of insulin resistance[51, 52] . When insulin sensitivity is reduced, glucose uptake in muscle cells decreases, creating an excess of glucose.

This excess is converted in the liver via de novo lipogenesis into triglycerides, which are then packaged in VLDL lipoprotein and subsequently partially transferred in the blood to HDL. This raises the triglyceride content of HDL and lowers HDL cholesterol levels[35].

A rise in triglycerides combined with a fall in HDL cholesterol is a classic and highly characteristic lipid pattern for insulin resistance. This explains why the Trig/HDL ratio is a reliable indicator of insulin resistance.

Within Synapse, the following thresholds are applied:

	Trig/HDL
Low risk	<2
Moderate risk	2 to 2.5
High risk	2.5 to 3
Very high risk	> 3

TYG

In addition to the HOMA index and the triglyceride/HDL ratio, the TyG index[36] is also frequently cited as a surrogate marker for insulin resistance. This index is calculated using fasting triglyceride and plasma glucose values and is therefore relatively inexpensive and accessible.

Note: There is debate about the precise calculation method for the TyG index. Synapse uses the formula above, with cut-off values[53]

	TyG Index
Low risk	<8.4
Moderate risk	8.4 to 8.75
High risk	8.75 to 9.1
Very high risk	> 9.1

Waist circumference

An important indicator of insulin resistance (IR) is the amount of visceral fat, which can be simply assessed via waist circumference. This method is non-invasive, inexpensive, and easy to apply in everyday practice. To also account for height, Synapse has opted to use the waist-to-height ratio, a parameter that is strongly associated with the development of IR and the risk of developing diabetes[54].

At Synapse, we apply the following cut-off values for waist circumference[4, 5] :

	Waist circumference / height
Low risk	<0.5
Moderate risk	0.5 - 0.6
High risk	> 0.6

Lifestyle factors

Insulin resistance often develops as a result of an unhealthy lifestyle. To assess not only the current degree of insulin resistance but also the future risk, Synapse integrates a range of lifestyle parameters into the evaluation. This gives us a more complete picture of both current and potential future insulin resistance.

Smoking

Smoking not only directly reduces insulin sensitivity[55] but also amplifies chronic inflammatory processes. Prolonged smoking also contributes to elevated plasma concentrations of free fatty acids (FFA). Hepatocytes and adipocytes will convert these FFAs into triglycerides, further promoting insulin resistance[56, 57] .

Exercise

Because insulin resistance largely develops in muscle cells, physical activity is essential for both preventing and managing this condition. Prolonged sitting can lead to insulin resistance developing even at a young age, as research in lean individuals in their twenties demonstrates[35, 58] .

At Synapse, we therefore assess various forms of physical activity: strength training[59], zone 2 endurance training[60], high-intensity zone 5 exercise[61], walking[62], and the degree of sedentary behaviour[58]. Each of these forms of exercise demonstrably contributes to the prevention and management of insulin resistance.

Insulin-disrupting diet

An unhealthy dietary pattern is an important factor in the development of insulin resistance. In particular, fast-digesting carbohydrates, processed food, insufficient fibre intake, and disrupted meal timing have been associated with large glycaemic fluctuations and the development of insulin resistance[63, 64, 65] .

Thyroid

Despite its small size, the thyroid gland has a profound influence on our metabolism[66]. By producing the hormones thyroxine (T4) and triiodothyronine (T3), it regulates essential processes such as energy expenditure, growth, and heat production. Thyroid abnormalities, even when subclinical, have a significant effect on metabolic health[67, 68, 69] .

TSH

TSH (thyroid-stimulating hormone) is produced by the pituitary gland in the brain and regulates the production of thyroid hormones (T4 and T3). An abnormal TSH value can indicate an underactive or overactive thyroid and therefore has direct consequences for metabolism.

Within Synapse, the following cut-off values[70, 71] are applied for TSH:

Thyroid function	TSH
Overactive	<0.27 mU/L
Possibly overactive	0.27 to 0.45 mU/L
Optimal	0.45 to 3.5 mU/L
Possibly underactive	3.5 to 4.2 mU/L
Underactive	> 4.2 mU/L

FT4

T4 (thyroxine) is the main hormone produced by the thyroid gland. A portion of T4 is converted in the body into T3, the biologically more active form. Because T4 provides direct insight into thyroid function and therefore into metabolism, it is an essential biomarker for the early detection of hypo- or hyperthyroidism.

Within Synapse, the following cut-off values[70] are applied for T4:

Thyroid function	FT4 (pmol/L)
Underactive	<11 pmol/L
Optimal	11 to 24 pmol/L
Overactive	> 24 pmol/L

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